
#pip install openpyxl


# importing libraries for data handling and analysis
import pandas as pd
pd.set_option('display.max_columns', 200)
pd.set_option('display.max_rows', 150)
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

# importing libraries for warnings
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=UserWarning, module="sklearn.cluster._kmeans")
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", message="The figure layout has changed to tight", category=UserWarning)
import os
os.environ["OMP_NUM_THREADS"] = "1"

# importing libraries for data visualisations
import plotly
import plotly.express as px
import plotly.figure_factory as ff
import plotly.graph_objects as go
%matplotlib inline

# sklearn modules for preprocessing and clustering
from sklearn.preprocessing import StandardScaler, RobustScaler,MinMaxScaler
from sklearn.cluster import KMeans
from pyclustertend import hopkins
from yellowbrick.cluster import KElbowVisualizer
from sklearn.metrics import silhouette_score
from scipy.cluster.hierarchy import dendrogram, linkage
from sklearn.cluster import AgglomerativeClustering


df = pd.read_excel("World military power.xlsx",header=[0,1]) # because of multyindex.
df.head()
# the smaller the power index, the more powerful a nation's conventional fighting capabilities.


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 138 entries, 0 to 137
Data columns (total 60 columns):
 #   Column                                                        Non-Null Count  Dtype  
---  ------                                                        --------------  -----  
 0   (2020 ranking, Military Strength)                             138 non-null    object 
 1   (2020 ranking, Military Strength Power Index)                 138 non-null    float64
 2   (Airforce Strength, Aircraft Strength)                        138 non-null    object 
 3   (Airforce Strength, Aircraft Strength value)                  138 non-null    int64  
 4   (Airforce Strength, Fighter/Interceptor Strength)             138 non-null    object 
 5   (Airforce Strength, Fighter/Interceptor Strength value)       138 non-null    int64  
 6   (Airforce Strength, Attack Aircraft Strength)                 138 non-null    object 
 7   (Airforce Strength, Attack Aircraft Strength value)           138 non-null    int64  
 8   (Airforce Strength, Transport Aircraft Fleet Strength)        138 non-null    object 
 9   (Airforce Strength, Transport Aircraft Fleet Strength value)  138 non-null    int64  
 10  (Airforce Strength, Trainer Aircraft Fleet)                   138 non-null    object 
 11  (Airforce Strength, Trainer Aircraft Fleet value)             138 non-null    int64  
 12  (Airforce Strength, Helicopter Fleet Strength)                138 non-null    object 
 13  (Airforce Strength, Helicopter Fleet Strength value)          138 non-null    int64  
 14  (Airforce Strength, Attack Helicopter Fleet Strength)         138 non-null    object 
 15  (Airforce Strength, Attack Helicopter Fleet Strength value)   138 non-null    int64  
 16  (Land Strength, Tank Strength)                                138 non-null    object 
 17  (Land Strength, Tank Strength value)                          138 non-null    int64  
 18  (Land Strength, AFV/APC Strength)                             138 non-null    object 
 19  (Land Strength, AFV/APC Strength value)                       138 non-null    int64  
 20  (Land Strength, Self-Propelled Artillery Strength)            138 non-null    object 
 21  (Land Strength, Self-Propelled Artillery Strength value)      138 non-null    int64  
 22  (Land Strength, Towed Artillery Strength)                     138 non-null    object 
 23  (Land Strength, Towed Artillery Strength value)               138 non-null    int64  
 24  (Land Strength, Rocket Projector Strength)                    138 non-null    object 
 25  (Land Strength, Rocket Projector Strength value)              138 non-null    int64  
 26  (Navy Strength, Navy Fleet Strengths)                         124 non-null    object 
 27  (Navy Strength, Navy Fleet Strengths value)                   124 non-null    float64
 28  (Navy Strength, Aircraft Carrier Fleet Strength)              115 non-null    object 
 29  (Navy Strength, Aircraft Carrier Fleet Strength value)        115 non-null    float64
 30  (Navy Strength, Submarine Fleet Strength)                     115 non-null    object 
 31  (Navy Strength, Submarine Fleet Strength value)               115 non-null    float64
 32  (Navy Strength, Destroyer Fleet Strength)                     115 non-null    object 
 33  (Navy Strength, Destroyer Fleet Strength value)               115 non-null    float64
 34  (Navy Strength, Frigate Fleet Strength)                       115 non-null    object 
 35  (Navy Strength, Frigate Fleet Strength value)                 115 non-null    float64
 36  (Finances, defense spending budget)                           138 non-null    object 
 37  (Finances, defense spending budget value)                     138 non-null    object 
 38  (Finances, External Debt)                                     138 non-null    object 
 39  (Finances, External Debt value)                               138 non-null    object 
 40  (Logistics, Airport Totals)                                   138 non-null    object 
 41  (Logistics, Airport Totals value)                             138 non-null    int64  
 42  (Natural resources, Oil Production)                           138 non-null    object 
 43  (Natural resources, Oil Production value)                     138 non-null    object 
 44  (Natural resources, Oil Consumption)                          138 non-null    object 
 45  (Natural resources, Oil Consumption value)                    138 non-null    object 
 46  (Natural resources, Proven Oil Reserves)                      138 non-null    object 
 47  (Natural resources, Proven Oil Reserves value)                138 non-null    object 
 48  (Manpower, Available Manpower)                                138 non-null    object 
 49  (Manpower, Available Manpower value)                          138 non-null    object 
 50  (Manpower, Total Population)                                  138 non-null    object 
 51  (Manpower, Total Population value)                            138 non-null    object 
 52  (Geography, Total Square Land Area)                           138 non-null    object 
 53  (Geography, Total Square Land Area value)                     138 non-null    object 
 54  (Geography, Total Coastline Coverage)                         109 non-null    object 
 55  (Geography, Total Coastline Coverage value)                   109 non-null    object 
 56  (Geography, Total Waterway Coverage)                          138 non-null    object 
 57  (Geography, Total Waterway Coverage value)                    138 non-null    object 
 58  (Geography, Total Border Coverage)                            129 non-null    object 
 59  (Geography, Total Border Coverage value)                      129 non-null    float64
dtypes: float64(7), int64(13), object(40)
memory usage: 64.8+ KB


df.shape

(138, 60)


df.duplicated().sum()

0


df.isna().sum().any()

True


df.columns # Due to the MultiIndex structure, the column headers appear as tuples.

MultiIndex([(     '2020 ranking',                       'Military Strength'),
            (     '2020 ranking',           'Military Strength Power Index'),
            ('Airforce Strength',                       'Aircraft Strength'),
            ('Airforce Strength',                 'Aircraft Strength value'),
            ('Airforce Strength',            'Fighter/Interceptor Strength'),
            ('Airforce Strength',      'Fighter/Interceptor Strength value'),
            ('Airforce Strength',                'Attack Aircraft Strength'),
            ('Airforce Strength',          'Attack Aircraft Strength value'),
            ('Airforce Strength',       'Transport Aircraft Fleet Strength'),
            ('Airforce Strength', 'Transport Aircraft Fleet Strength value'),
            ('Airforce Strength',                  'Trainer Aircraft Fleet'),
            ('Airforce Strength',            'Trainer Aircraft Fleet value'),
            ('Airforce Strength',               'Helicopter Fleet Strength'),
            ('Airforce Strength',         'Helicopter Fleet Strength value'),
            ('Airforce Strength',        'Attack Helicopter Fleet Strength'),
            ('Airforce Strength',  'Attack Helicopter Fleet Strength value'),
            (    'Land Strength',                           'Tank Strength'),
            (    'Land Strength',                     'Tank Strength value'),
            (    'Land Strength',                        'AFV/APC Strength'),
            (    'Land Strength',                  'AFV/APC Strength value'),
            (    'Land Strength',       'Self-Propelled Artillery Strength'),
            (    'Land Strength', 'Self-Propelled Artillery Strength value'),
            (    'Land Strength',                'Towed Artillery Strength'),
            (    'Land Strength',          'Towed Artillery Strength value'),
            (    'Land Strength',               'Rocket Projector Strength'),
            (    'Land Strength',         'Rocket Projector Strength value'),
            (    'Navy Strength',                    'Navy Fleet Strengths'),
            (    'Navy Strength',              'Navy Fleet Strengths value'),
            (    'Navy Strength',         'Aircraft Carrier Fleet Strength'),
            (    'Navy Strength',   'Aircraft Carrier Fleet Strength value'),
            (    'Navy Strength',                'Submarine Fleet Strength'),
            (    'Navy Strength',          'Submarine Fleet Strength value'),
            (    'Navy Strength',                'Destroyer Fleet Strength'),
            (    'Navy Strength',          'Destroyer Fleet Strength value'),
            (    'Navy Strength',                  'Frigate Fleet Strength'),
            (    'Navy Strength',            'Frigate Fleet Strength value'),
            (         'Finances',                 'defense spending budget'),
            (         'Finances',           'defense spending budget value'),
            (         'Finances',                           'External Debt'),
            (         'Finances',                     'External Debt value'),
            (        'Logistics',                          'Airport Totals'),
            (        'Logistics',                    'Airport Totals value'),
            ('Natural resources',                          'Oil Production'),
            ('Natural resources',                    'Oil Production value'),
            ('Natural resources',                         'Oil Consumption'),
            ('Natural resources',                   'Oil Consumption value'),
            ('Natural resources',                     'Proven Oil Reserves'),
            ('Natural resources',               'Proven Oil Reserves value'),
            (         'Manpower',                      'Available Manpower'),
            (         'Manpower',                'Available Manpower value'),
            (         'Manpower',                        'Total Population'),
            (         'Manpower',                  'Total Population value'),
            (        'Geography',                  'Total Square Land Area'),
            (        'Geography',            'Total Square Land Area value'),
            (        'Geography',                'Total Coastline Coverage'),
            (        'Geography',          'Total Coastline Coverage value'),
            (        'Geography',                 'Total Waterway Coverage'),
            (        'Geography',           'Total Waterway Coverage value'),
            (        'Geography',                   'Total Border Coverage'),
            (        'Geography',             'Total Border Coverage value')],
           )


# To convert each level of the tuple to a string for iterating through the MultiIndex column headers.
df.columns = ['_'.join(tuple(map(str, t))) for t in df.columns.values]


df.columns

Index(['2020 ranking_Military Strength',
       '2020 ranking_Military Strength Power Index',
       'Airforce Strength_Aircraft Strength',
       'Airforce Strength_Aircraft Strength value',
       'Airforce Strength_Fighter/Interceptor Strength',
       'Airforce Strength_Fighter/Interceptor Strength value',
       'Airforce Strength_Attack Aircraft Strength',
       'Airforce Strength_Attack Aircraft Strength value',
       'Airforce Strength_Transport Aircraft Fleet Strength',
       'Airforce Strength_Transport Aircraft Fleet Strength value',
       'Airforce Strength_Trainer Aircraft Fleet',
       'Airforce Strength_Trainer Aircraft Fleet value',
       'Airforce Strength_Helicopter Fleet Strength',
       'Airforce Strength_Helicopter Fleet Strength value',
       'Airforce Strength_Attack Helicopter Fleet Strength',
       'Airforce Strength_Attack Helicopter Fleet Strength value',
       'Land Strength_Tank Strength', 'Land Strength_Tank Strength value',
       'Land Strength_AFV/APC Strength',
       'Land Strength_AFV/APC Strength value',
       'Land Strength_Self-Propelled Artillery Strength',
       'Land Strength_Self-Propelled Artillery Strength value',
       'Land Strength_Towed Artillery Strength',
       'Land Strength_Towed Artillery Strength value',
       'Land Strength_Rocket Projector Strength',
       'Land Strength_Rocket Projector Strength value',
       'Navy Strength_Navy Fleet Strengths',
       'Navy Strength_Navy Fleet Strengths value',
       'Navy Strength_Aircraft Carrier Fleet Strength',
       'Navy Strength_Aircraft Carrier Fleet Strength value',
       'Navy Strength_Submarine Fleet Strength',
       'Navy Strength_Submarine Fleet Strength value',
       'Navy Strength_Destroyer Fleet Strength',
       'Navy Strength_Destroyer Fleet Strength value',
       'Navy Strength_Frigate Fleet Strength',
       'Navy Strength_Frigate Fleet Strength value',
       'Finances_defense spending budget',
       'Finances_defense spending budget value', 'Finances_External Debt',
       'Finances_External Debt value', 'Logistics_Airport Totals',
       'Logistics_Airport Totals value', 'Natural resources_Oil Production',
       'Natural resources_Oil Production value',
       'Natural resources_Oil Consumption',
       'Natural resources_Oil Consumption value',
       'Natural resources_Proven Oil Reserves',
       'Natural resources_Proven Oil Reserves value',
       'Manpower_Available Manpower', 'Manpower_Available Manpower value',
       'Manpower_Total Population', 'Manpower_Total Population value',
       'Geography_Total Square Land Area',
       'Geography_Total Square Land Area value',
       'Geography_Total Coastline Coverage',
       'Geography_Total Coastline Coverage value',
       'Geography_Total Waterway Coverage',
       'Geography_Total Waterway Coverage value',
       'Geography_Total Border Coverage',
       'Geography_Total Border Coverage value'],
      dtype='object')


df.head()


# to find all occurrences of the pattern ".*value" within each column name. 
value_cols = [i[0] for i in df.columns.str.findall(".*value") if i !=[]]

value_cols

['Airforce Strength_Aircraft Strength value',
 'Airforce Strength_Fighter/Interceptor Strength value',
 'Airforce Strength_Attack Aircraft Strength value',
 'Airforce Strength_Transport Aircraft Fleet Strength value',
 'Airforce Strength_Trainer Aircraft Fleet value',
 'Airforce Strength_Helicopter Fleet Strength value',
 'Airforce Strength_Attack Helicopter Fleet Strength value',
 'Land Strength_Tank Strength value',
 'Land Strength_AFV/APC Strength value',
 'Land Strength_Self-Propelled Artillery Strength value',
 'Land Strength_Towed Artillery Strength value',
 'Land Strength_Rocket Projector Strength value',
 'Navy Strength_Navy Fleet Strengths value',
 'Navy Strength_Aircraft Carrier Fleet Strength value',
 'Navy Strength_Submarine Fleet Strength value',
 'Navy Strength_Destroyer Fleet Strength value',
 'Navy Strength_Frigate Fleet Strength value',
 'Finances_defense spending budget value',
 'Finances_External Debt value',
 'Logistics_Airport Totals value',
 'Natural resources_Oil Production value',
 'Natural resources_Oil Consumption value',
 'Natural resources_Proven Oil Reserves value',
 'Manpower_Available Manpower value',
 'Manpower_Total Population value',
 'Geography_Total Square Land Area value',
 'Geography_Total Coastline Coverage value',
 'Geography_Total Waterway Coverage value',
 'Geography_Total Border Coverage value']


# Create a copy of the original DataFrame to avoid modifying it directly if you don't want to
value_df = df.copy()

# Select the "value" columns
value_df = value_df[value_cols]

# Insert the "country" column
value_df.insert(0, "country", df["2020 ranking_Military Strength"])

# Insert the "Military Strength Power Index" column
value_df.insert(1, "Military Strength Power Index", df["2020 ranking_Military Strength Power Index"])

# Now, if you want to rename 'value_df' back to 'df':
df = value_df.copy() # Use .copy() to avoid potential SettingWithCopyWarning
df.head()
#We've gotten rid of unnecessary, repeated columns.


df.isnull().sum().sort_values(ascending=False)

Geography_Total Coastline Coverage value                     29
Navy Strength_Aircraft Carrier Fleet Strength value          23
Navy Strength_Frigate Fleet Strength value                   23
Navy Strength_Destroyer Fleet Strength value                 23
Navy Strength_Submarine Fleet Strength value                 23
Navy Strength_Navy Fleet Strengths value                     14
Geography_Total Border Coverage value                         9
Airforce Strength_Helicopter Fleet Strength value             0
Airforce Strength_Attack Helicopter Fleet Strength value      0
Geography_Total Waterway Coverage value                       0
Airforce Strength_Aircraft Strength value                     0
Geography_Total Square Land Area value                        0
Manpower_Total Population value                               0
Manpower_Available Manpower value                             0
Natural resources_Proven Oil Reserves value                   0
Natural resources_Oil Consumption value                       0
Natural resources_Oil Production value                        0
Logistics_Airport Totals value                                0
Finances_External Debt value                                  0
Finances_defense spending budget value                        0
Airforce Strength_Fighter/Interceptor Strength value          0
Airforce Strength_Attack Aircraft Strength value              0
Airforce Strength_Transport Aircraft Fleet Strength value     0
Military Strength Power Index                                 0
Airforce Strength_Trainer Aircraft Fleet value                0
Land Strength_Rocket Projector Strength value                 0
Land Strength_Towed Artillery Strength value                  0
Land Strength_Self-Propelled Artillery Strength value         0
Land Strength_AFV/APC Strength value                          0
Land Strength_Tank Strength value                             0
country                                                       0
dtype: int64


df.isnull().melt(value_name="missing")#unpivoting the table from a wide format to a long format


sns.displot(
    data=df.isnull().melt(value_name="missing"),
    y="variable",
    hue="missing",
    multiple="fill",
    height=4,
    aspect=1.5
)
plt.axvline(0.25, color="r")
plt.subplots_adjust(left=0.1, right=0.8, bottom=0.1, top=0.9) # Increased left margin
plt.show()


with pd.option_context('display.max_columns', None):
    display(df[df["Geography_Total Coastline Coverage value"].isna()])
# to inspect the rows where the "Geography_Total Coastline Coverage" column has missing values or landlocked countries.


df.loc[df["Geography_Total Coastline Coverage value"].isnull(), ['country',
                                                                             'Navy Strength_Navy Fleet Strengths value',
                                                                             'Navy Strength_Aircraft Carrier Fleet Strength value',
                                                                             'Navy Strength_Submarine Fleet Strength value',
                                                                             'Navy Strength_Destroyer Fleet Strength value',
                                                                             'Navy Strength_Frigate Fleet Strength value',
                                                                             'Geography_Total Waterway Coverage value']]


df.loc[df["country"] == "South Sudan",
             ['country',
             'Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]]


df.loc[df["country"] == "South Sudan", 'Navy Strength_Aircraft Carrier Fleet Strength value'] = 0
df.loc[df["country"] == "South Sudan", 'Navy Strength_Submarine Fleet Strength value'] = 0
df.loc[df["country"] == "South Sudan", 'Navy Strength_Destroyer Fleet Strength value'] = 0
df.loc[df["country"] == "South Sudan", 'Navy Strength_Frigate Fleet Strength value'] = 0


df.loc[
    
    df["Geography_Total Coastline Coverage value"].isnull(),
    
    ['Navy Strength_Navy Fleet Strengths value',
     'Navy Strength_Aircraft Carrier Fleet Strength value',
     'Navy Strength_Submarine Fleet Strength value',
     'Navy Strength_Destroyer Fleet Strength value',
     'Navy Strength_Frigate Fleet Strength value']
    
    ] = 0


sns.displot(
    data=df.isnull().melt(value_name="missing"),
    y="variable",
    hue="missing",
    multiple="fill",
    height=4,
    aspect=1.5
)
plt.axvline(0.25, color="r")
plt.subplots_adjust(left=0.1, right=0.8, bottom=0.1, top=0.9) # Increased left margin
plt.show()


df.loc[df['Navy Strength_Aircraft Carrier Fleet Strength value'].isnull()|
             df['Navy Strength_Submarine Fleet Strength value'].isnull()|
             df['Navy Strength_Destroyer Fleet Strength value'].isnull()|
             df['Navy Strength_Frigate Fleet Strength value'].isnull()]


df.loc[df["country"] == "Uzbekistan",
            ['Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]]


df.loc[df["country"] == "Uzbekistan",
            ['Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]] = 0


df.loc[df["country"] == "South Korea",
            ['Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]]


df.loc[((df['Navy Strength_Navy Fleet Strengths value'] > 200) & 
             (df['Navy Strength_Navy Fleet Strengths value'] < 250)),
            ['country',
             'Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]]


df.loc[df["country"] == "France",
             ['country',
             'Navy Strength_Navy Fleet Strengths value',
             'Navy Strength_Aircraft Carrier Fleet Strength value',
             'Navy Strength_Submarine Fleet Strength value',
             'Navy Strength_Destroyer Fleet Strength value',
             'Navy Strength_Frigate Fleet Strength value',
           ]]


df.loc[df["country"] == "South Korea"]


# All the numbers derived from South Korea Navy https://www.globalfirepower.com/
df.loc[df["country"] == "South Korea",'Navy Strength_Aircraft Carrier Fleet Strength value'] = 0
df.loc[df["country"] == "South Korea",'Navy Strength_Submarine Fleet Strength value'] = 19
df.loc[df["country"] == "South Korea",'Navy Strength_Destroyer Fleet Strength value'] = 12
df.loc[df["country"] == "South Korea",'Navy Strength_Frigate Fleet Strength value'] = 12


df.loc[df["country"] == "South Korea"]


sns.displot(
    data=df.isnull().melt(value_name="missing"),
    y="variable",
    hue="missing",
    multiple="fill",
    height=4,
    aspect=1.5
)
plt.axvline(0.25, color="r")
plt.subplots_adjust(left=0.1, right=0.8, bottom=0.1, top=0.9) # Increased left margin
plt.show()


df.loc[df['Geography_Total Coastline Coverage value'].isnull()]


df.loc[df['Geography_Total Coastline Coverage value'].isnull(), 
                      'Geography_Total Coastline Coverage value']  = 0


df.loc[df['Geography_Total Border Coverage value'].isnull()]


df.loc[df['Geography_Total Border Coverage value'].isnull(), 
                      'Geography_Total Border Coverage value' ] = 0


sns.displot(
    data=df.isnull().melt(value_name="missing"),
    y="variable",
    hue="missing",
    multiple="fill",
    height=4,
    aspect=1.5
)
plt.axvline(0.25, color="r")
plt.subplots_adjust(left=0.1, right=0.8, bottom=0.1, top=0.9) # Increased left margin
plt.show()


df.reset_index(drop=True,inplace=True)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 138 entries, 0 to 137
Data columns (total 31 columns):
 #   Column                                                     Non-Null Count  Dtype  
---  ------                                                     --------------  -----  
 0   country                                                    138 non-null    object 
 1   Military Strength Power Index                              138 non-null    float64
 2   Airforce Strength_Aircraft Strength value                  138 non-null    int64  
 3   Airforce Strength_Fighter/Interceptor Strength value       138 non-null    int64  
 4   Airforce Strength_Attack Aircraft Strength value           138 non-null    int64  
 5   Airforce Strength_Transport Aircraft Fleet Strength value  138 non-null    int64  
 6   Airforce Strength_Trainer Aircraft Fleet value             138 non-null    int64  
 7   Airforce Strength_Helicopter Fleet Strength value          138 non-null    int64  
 8   Airforce Strength_Attack Helicopter Fleet Strength value   138 non-null    int64  
 9   Land Strength_Tank Strength value                          138 non-null    int64  
 10  Land Strength_AFV/APC Strength value                       138 non-null    int64  
 11  Land Strength_Self-Propelled Artillery Strength value      138 non-null    int64  
 12  Land Strength_Towed Artillery Strength value               138 non-null    int64  
 13  Land Strength_Rocket Projector Strength value              138 non-null    int64  
 14  Navy Strength_Navy Fleet Strengths value                   138 non-null    float64
 15  Navy Strength_Aircraft Carrier Fleet Strength value        138 non-null    float64
 16  Navy Strength_Submarine Fleet Strength value               138 non-null    float64
 17  Navy Strength_Destroyer Fleet Strength value               138 non-null    float64
 18  Navy Strength_Frigate Fleet Strength value                 138 non-null    float64
 19  Finances_defense spending budget value                     138 non-null    object 
 20  Finances_External Debt value                               138 non-null    object 
 21  Logistics_Airport Totals value                             138 non-null    int64  
 22  Natural resources_Oil Production value                     138 non-null    object 
 23  Natural resources_Oil Consumption value                    138 non-null    object 
 24  Natural resources_Proven Oil Reserves value                138 non-null    object 
 25  Manpower_Available Manpower value                          138 non-null    object 
 26  Manpower_Total Population value                            138 non-null    object 
 27  Geography_Total Square Land Area value                     138 non-null    object 
 28  Geography_Total Coastline Coverage value                   138 non-null    object 
 29  Geography_Total Waterway Coverage value                    138 non-null    object 
 30  Geography_Total Border Coverage value                      138 non-null    float64
dtypes: float64(7), int64(13), object(11)
memory usage: 33.5+ KB


df.select_dtypes("object")


df.select_dtypes("object")["Natural resources_Oil Production value"].str.replace(",","")
#cleaning numerical data that might be stored as strings

0           NaN
1           NaN
2       1306000
3       1666000
4        479400
5           NaN
6        263000
7           NaN
8        789300
9           NaN
10          NaN
11          NaN
12          NaN
13          NaN
14          NaN
15          NaN
16          NaN
17      2622000
18          NaN
19          NaN
20          NaN
21          NaN
22      3977000
23          NaN
24       128000
25          NaN
26      3838000
27       853600
28          NaN
29          NaN
30          NaN
31          NaN
32       137100
33          NaN
34       531300
35       589400
36          NaN
37          NaN
38          NaN
39          NaN
40          NaN
41       198800
42          NaN
43          NaN
44       151000
45          NaN
46          NaN
47          NaN
48          NaN
49       733900
50       801700
51      4469000
52      4454000
53          NaN
54          NaN
55          NaN
56          NaN
57          NaN
58          NaN
59      1777000
60          NaN
61      2753000
62          NaN
63          NaN
64          NaN
65          NaN
66          NaN
67       897100
68          NaN
69          NaN
70       647900
71          NaN
72          NaN
73      1981000
74          NaN
75          NaN
76          NaN
77          NaN
78          NaN
79          NaN
80          NaN
81          NaN
82          NaN
83          NaN
84          NaN
85          NaN
86      1946000
87          NaN
88          NaN
89      1618000
90       970400
91          NaN
92          NaN
93          NaN
94          NaN
95          NaN
96          NaN
97          NaN
98      1500000
99       244200
100         NaN
101    10580000
102    10130000
103         NaN
104         NaN
105         NaN
106         NaN
107         NaN
108         NaN
109         NaN
110         NaN
111      150200
112         NaN
113         NaN
114      102300
115         NaN
116         NaN
117         NaN
118         NaN
119         NaN
120         NaN
121         NaN
122      239700
123         NaN
124      245000
125      230880
126         NaN
127         NaN
128     3174000
129      910500
130     9352000
131         NaN
132         NaN
133     2007000
134      271400
135         NaN
136         NaN
137         NaN
Name: Natural resources_Oil Production value, dtype: object


columns = df.select_dtypes("object").columns
columns

Index(['country', 'Finances_defense spending budget value',
       'Finances_External Debt value',
       'Natural resources_Oil Production value',
       'Natural resources_Oil Consumption value',
       'Natural resources_Proven Oil Reserves value',
       'Manpower_Available Manpower value', 'Manpower_Total Population value',
       'Geography_Total Square Land Area value',
       'Geography_Total Coastline Coverage value',
       'Geography_Total Waterway Coverage value'],
      dtype='object')


for col in columns[1:]:
    df[col]  = df[col].astype("str")
    df[col]  = df.select_dtypes("object")[col].str.replace(',','')
    df[col]  = df[col].astype("float")
    # to clean and convert a range of columns to numerical (float) format.


 df.select_dtypes("object")


df.columns

Index(['country', 'Military Strength Power Index',
       'Airforce Strength_Aircraft Strength value',
       'Airforce Strength_Fighter/Interceptor Strength value',
       'Airforce Strength_Attack Aircraft Strength value',
       'Airforce Strength_Transport Aircraft Fleet Strength value',
       'Airforce Strength_Trainer Aircraft Fleet value',
       'Airforce Strength_Helicopter Fleet Strength value',
       'Airforce Strength_Attack Helicopter Fleet Strength value',
       'Land Strength_Tank Strength value',
       'Land Strength_AFV/APC Strength value',
       'Land Strength_Self-Propelled Artillery Strength value',
       'Land Strength_Towed Artillery Strength value',
       'Land Strength_Rocket Projector Strength value',
       'Navy Strength_Navy Fleet Strengths value',
       'Navy Strength_Aircraft Carrier Fleet Strength value',
       'Navy Strength_Submarine Fleet Strength value',
       'Navy Strength_Destroyer Fleet Strength value',
       'Navy Strength_Frigate Fleet Strength value',
       'Finances_defense spending budget value',
       'Finances_External Debt value', 'Logistics_Airport Totals value',
       'Natural resources_Oil Production value',
       'Natural resources_Oil Consumption value',
       'Natural resources_Proven Oil Reserves value',
       'Manpower_Available Manpower value', 'Manpower_Total Population value',
       'Geography_Total Square Land Area value',
       'Geography_Total Coastline Coverage value',
       'Geography_Total Waterway Coverage value',
       'Geography_Total Border Coverage value'],
      dtype='object')


df.drop(['Geography_Total Square Land Area value',
               'Geography_Total Coastline Coverage value',
               'Geography_Total Waterway Coverage value',
               'Geography_Total Border Coverage value'],axis=1,inplace=True)


fig = px.bar(df,x= "country" , y = value_df["Airforce Strength_Aircraft Strength value"])
fig.show()


fig = px.box(df, x = value_df["Airforce Strength_Aircraft Strength value"],
             hover_name=value_df["country"])
fig.show()


fig = px.bar(df,x= "country" , y = value_df['Navy Strength_Navy Fleet Strengths value'])
fig.show()


fig = px.box(df, x = value_df["Navy Strength_Navy Fleet Strengths value"],
             hover_name=value_df["country"])

fig.show()


fig = px.bar(df,x= "country" , y = value_df['Land Strength_Tank Strength value'])
fig.show()


fig = px.box(df, x = value_df["Land Strength_Tank Strength value"],
             hover_name=value_df["country"])

fig.show()


fig = go.Figure()
fig.add_trace(go.Box(x=df["Finances_External Debt value"],
              hovertext=df["country"],
              name = "Finances_External Debt value")
             )
fig.add_trace(go.Box(x=df["Finances_defense spending budget value"],
                     hovertext=df["country"],
                    name = "Finances_defense spending budget value")
             )

fig.show()


df.describe().T


plt.figure(figsize=(15, 10))
sns.heatmap(df.corr(), annot=True);


fig = px.imshow(df.corr(), width=1000, height=800)
fig.show()


X = df.drop(["country"], axis=1)


scaler = StandardScaler()
X = scaler.fit_transform(X)


hopkins(X, X.shape[0])

0.07456565327454581


ssd = [] # sum of squared distance

K = range(2,10)

for k in K:
    model = KMeans(n_clusters=k)
    model.fit(X)
    ssd.append(model.inertia_)
# Lower inertia generally indicates that the data points within each cluster are closer to their cluster's centroid. 
#This suggests that the clusters are more compact and internally similar. Ideally, you want clusters that are tightly grouped.

ssd

[1908.9606723107413,
 1293.9804803548773,
 1033.3079025228537,
 871.3919363344244,
 726.3028244291534,
 596.6149495076263,
 498.02807094870917,
 438.45503320650994]


plt.plot(K, ssd, "bo-")
plt.xlabel("Different k values")
plt.ylabel("inertia-error") 
plt.title("elbow method")

Text(0.5, 1.0, 'elbow method')


pd.Series(ssd).diff()

0           NaN
1   -614.980192
2   -260.672578
3   -161.915966
4   -145.089112
5   -129.687875
6    -98.586879
7    -59.573038
dtype: float64


df1_diff =pd.DataFrame(-pd.Series(ssd).diff()).rename(index = lambda x : x+1)
df1_diff


df1_diff.plot(kind='bar');


kmeans = KMeans()
visu = KElbowVisualizer(kmeans, k = (2,10))
visu.fit(X)
visu.poof();


silhouette_score(X, kmeans.labels_)

0.2752576078156073


range_n_clusters = range(2,9)
for num_clusters in range_n_clusters:
    # intialise kmeans
    kmeans = KMeans(n_clusters=num_clusters, random_state=101)
    kmeans.fit(X)
    cluster_labels = kmeans.labels_
    # silhouette score
    silhouette_avg = silhouette_score(X, cluster_labels)
    print(f"For n_clusters={num_clusters}, the silhouette score is {silhouette_avg}")

For n_clusters=2, the silhouette score is 0.8645343457024808
For n_clusters=3, the silhouette score is 0.8021734897138548
For n_clusters=4, the silhouette score is 0.6431108598819103
For n_clusters=5, the silhouette score is 0.4970990294193429
For n_clusters=6, the silhouette score is 0.5059656486613938
For n_clusters=7, the silhouette score is 0.4861811103301968
For n_clusters=8, the silhouette score is 0.4388025628710392


model = KMeans(n_clusters=3)
model.fit(X)

KMeans(n_clusters=3)

KMeans(n_clusters=3)


model.inertia_

1318.9857111090864


model.labels_

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       0, 0, 0, 0, 0, 0])


df["cluster"] = model.labels_ # label değerleri cluster sütunu olarak atıyoruz.


plt.figure(figsize=(10,5))
sns.scatterplot(data=df,  # You need to pass your DataFrame using the 'data=' keyword
                x="Airforce Strength_Aircraft Strength value",
                y="Navy Strength_Navy Fleet Strengths value",
                hue="cluster",
                palette="viridis");


fig = px.scatter_3d(x=df["Airforce Strength_Aircraft Strength value"],
                   y=df["Finances_defense spending budget value"],
                   z=df["Finances_External Debt value"],
                   color=df.cluster,
                   labels = {"x": "Airforce Strength_Aircraft Strength value",
                             "y": "Finances_defense spending budget value",
                             "z": "Finances_External Debt value"},
                   hover_name=df["country"])

fig.show()


centroids = model.cluster_centers_

plt.scatter(X[:,18], X[:,19], c = df.cluster, s = 50, cmap = "viridis")
plt.scatter(centroids[:,18], centroids[:,19], c = "black", s = 200, alpha=0.5);


plt.figure(figsize=(8,5))
plt.title("Cluster Count")
ax1 = sns.countplot(x = df.cluster)

for p in ax1.patches:
    ax1.annotate(
        text=f"{p.get_height():1.0f}",  # Changed 's' to 'text'
        xy=(p.get_x() + p.get_width() / 2., p.get_height()),
        xycoords='data',
        ha='center',
        va='center',
        fontsize=11,
        color='black',
        xytext=(0,7),
        textcoords='offset points',
        clip_on=True
    )
plt.show()


df[df["cluster"] == 0]


df[df["cluster"] == 1]


df[df["cluster"] == 2]


print(f'Silhouette Score(n=4): {silhouette_score(X, df.cluster)}') # towards to 1 means, data good clustered by model

Silhouette Score(n=4): 0.8388682569557854


range_n_clusters = range(2,9)
for num_clusters in range_n_clusters:
    # intialise kmeans
    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
    kmeans.fit(X)
    cluster_labels = kmeans.labels_
    # silhouette score
    silhouette_avg = silhouette_score(X, cluster_labels)
    print(f"For n_clusters={num_clusters}, the silhouette score is {silhouette_avg}")

For n_clusters=2, the silhouette score is 0.8645343457024808
For n_clusters=3, the silhouette score is 0.8194441293083203
For n_clusters=4, the silhouette score is 0.4955821939029867
For n_clusters=5, the silhouette score is 0.5176044245264125
For n_clusters=6, the silhouette score is 0.4865589638170736
For n_clusters=7, the silhouette score is 0.4861811103301968
For n_clusters=8, the silhouette score is 0.4769827086899002


kmean = pd.DataFrame(X,columns=df.drop(["country","cluster"],axis=1).columns)
kmean["cluster"] = df["cluster"]

clus_pred_kmean = kmean.groupby("cluster").mean().T
clus_pred_kmean


fig= px.line(clus_pred_kmean)
fig.show()


from scipy.cluster.hierarchy import dendrogram, linkage
# yeni bir data geldiğinde direkt predict yapamayız, datanın içine atarak işlem yapmalıyız.


hc_ward = linkage(y = X, method = "ward") # eklendiği sınıfın varyansını düşürecek şekile ata
hc_complete = linkage(X, "complete") # en uzak noktaya göre en küçük değer
hc_average = linkage(X, "average")  # ortalama
hc_single = linkage(X, "single") #en yakın noktaya göre en küçük değer


plt.figure(figsize = (20,12))

plt.subplot(221)
plt.title("Ward")
plt.xlabel("Observations")
plt.ylabel("Distance")
dendrogram(hc_ward, leaf_font_size = 10, truncate_mode='lastp', p=10)

plt.subplot(222)
plt.title("Complete")
plt.xlabel("Observations")
plt.ylabel("Distance")
dendrogram(hc_complete, leaf_font_size = 10, truncate_mode='lastp', p=10)

plt.subplot(223)
plt.title("Average")
plt.xlabel("Observations")
plt.ylabel("Distance")
dendrogram(hc_average, leaf_font_size = 10, truncate_mode='lastp', p=10)

plt.subplot(224)
plt.title("Single")
plt.xlabel("Observations")
plt.ylabel("Distance")
dendrogram(hc_single, leaf_font_size = 10, truncate_mode='lastp', p=10);


from sklearn.cluster import AgglomerativeClustering
# elbow yapılmıyor


model1 = AgglomerativeClustering(n_clusters=4, affinity = "euclidean", linkage = "ward")
model1.fit_predict(X)

array([3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 1, 3, 3, 3, 3, 3, 1, 1, 3, 3, 3,
       1, 3, 3, 1, 0, 1, 3, 3, 3, 3, 1, 3, 3, 1, 3, 3, 3, 1, 1, 3, 3, 1,
       3, 1, 3, 3, 3, 0, 1, 1, 1, 3, 1, 1, 3, 1, 1, 3, 3, 1, 3, 3, 3, 3,
       3, 3, 3, 3, 1, 3, 3, 1, 3, 3, 3, 1, 3, 1, 3, 3, 1, 3, 3, 3, 3, 1,
       3, 3, 3, 1, 3, 3, 1, 3, 1, 1, 3, 3, 1, 0, 1, 3, 3, 1, 3, 3, 3, 1,
       1, 3, 1, 3, 3, 3, 3, 3, 1, 1, 3, 3, 1, 3, 1, 3, 3, 1, 1, 1, 2, 3,
       3, 1, 1, 3, 3, 3], dtype=int64)


range_n_clusters = range(2,9)
for num_clusters in range_n_clusters:
    # intialise kmeans
    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
    kmeans.fit(X)
    cluster_labels = kmeans.labels_
    # silhouette score
    silhouette_avg = silhouette_score(X, cluster_labels)
    print(f"For n_clusters={num_clusters}, the silhouette score is {silhouette_avg}")

For n_clusters=2, the silhouette score is 0.8645343457024808
For n_clusters=3, the silhouette score is 0.8194441293083203
For n_clusters=4, the silhouette score is 0.4955821939029867
For n_clusters=5, the silhouette score is 0.5176044245264125
For n_clusters=6, the silhouette score is 0.4865589638170736
For n_clusters=7, the silhouette score is 0.4861811103301968
For n_clusters=8, the silhouette score is 0.4769827086899002


df["cluster_1"] = model1.labels_


fig = px.scatter_3d(x=df["Airforce Strength_Aircraft Strength value"],
                   y=df["Finances_defense spending budget value"],
                   z=df["Finances_External Debt value"],
                   color=df.cluster_1,
                   labels = {"x": "Airforce Strength_Aircraft Strength value",
                             "y": "Finances_defense spending budget value",
                             "z": "Finances_External Debt value"},
                   hover_name=df["country"])

fig.show()


plt.figure(figsize=(15,10))
plt.title("Cluster Count")
ax1 = sns.countplot(x = df.cluster_1)

for p in ax1.patches:
    ax1.annotate(
        text=f"{p.get_height():1.0f}",
        xy=(p.get_x() + p.get_width() / 2., p.get_height()), 
        xycoords='data',
        ha='center', 
        va='center', 
        fontsize=11, 
        color='black',
        xytext=(0,7), 
        textcoords='offset points',
        clip_on=True,                   
    )
plt.show();


dend = pd.DataFrame(X,columns=df.drop(["country","cluster","cluster_1"],axis=1).columns)
dend["cluster"] = df["cluster_1"]

clus_pred_dend = dend.groupby("cluster").mean().T
clus_pred_dend


fig= px.line(clus_pred_dend)
fig.show()


df[df["cluster_1"] == 0]


df[df["cluster_1"] == 1]


df[df["cluster_1"] == 2]


df[df["cluster_1"] == 3]

	2020 ranking		Airforce Strength														Land Strength										Navy Strength										Finances				Logistics		Natural resources						Manpower				Geography
	Military Strength	Military Strength Power Index	Aircraft Strength	Aircraft Strength value	Fighter/Interceptor Strength	Fighter/Interceptor Strength value	Attack Aircraft Strength	Attack Aircraft Strength value	Transport Aircraft Fleet Strength	Transport Aircraft Fleet Strength value	Trainer Aircraft Fleet	Trainer Aircraft Fleet value	Helicopter Fleet Strength	Helicopter Fleet Strength value	Attack Helicopter Fleet Strength	Attack Helicopter Fleet Strength value	Tank Strength	Tank Strength value	AFV/APC Strength	AFV/APC Strength value	Self-Propelled Artillery Strength	Self-Propelled Artillery Strength value	Towed Artillery Strength	Towed Artillery Strength value	Rocket Projector Strength	Rocket Projector Strength value	Navy Fleet Strengths	Navy Fleet Strengths value	Aircraft Carrier Fleet Strength	Aircraft Carrier Fleet Strength value	Submarine Fleet Strength	Submarine Fleet Strength value	Destroyer Fleet Strength	Destroyer Fleet Strength value	Frigate Fleet Strength	Frigate Fleet Strength value	defense spending budget	defense spending budget value	External Debt	External Debt value	Airport Totals	Airport Totals value	Oil Production	Oil Production value	Oil Consumption	Oil Consumption value	Proven Oil Reserves	Proven Oil Reserves value	Available Manpower	Available Manpower value	Total Population	Total Population value	Total Square Land Area	Total Square Land Area value	Total Coastline Coverage	Total Coastline Coverage value	Total Waterway Coverage	Total Waterway Coverage value	Total Border Coverage	Total Border Coverage value
0	Afghanistan	1.3444	Afghanistan	260	Afghanistan	0	Afghanistan	25	Afghanistan	30	Afghanistan	0	Afghanistan	187	Afghanistan	0	Afghanistan	0	Afghanistan	1062	Afghanistan	0	Afghanistan	176	Afghanistan	50	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	12,00,00,00,000	Afghanistan	2,84,00,00,000	Afghanistan	43	Afghanistan	0	Afghanistan	5500	Afghanistan	0	Afghanistan	1,43,25,743	Afghanistan	3,49,40,837	Afghanistan	6,52,230	Afghanistan	0	Afghanistan	1200	Afghanistan	5987.0
1	Albania	2.3137	Albania	19	Albania	0	Albania	0	Albania	0	Albania	0	Albania	19	Albania	0	Albania	0	Albania	467	Albania	0	Albania	0	Albania	0	Albania	38.0	Albania	0.0	Albania	0.0	Albania	0.0	Albania	0.0	Albania	25,00,00,000	Albania	9,50,50,00,000	Albania	4	Albania	16000	Albania	42500	Albania	16,83,00,000	Albania	15,19,438	Albania	30,57,220	Albania	28748	Albania	362	Albania	41	Albania	691.0
2	Algeria	0.4659	Algeria	551	Algeria	103	Algeria	22	Algeria	59	Algeria	87	Algeria	257	Algeria	45	Algeria	880	Algeria	7361	Algeria	320	Algeria	240	Algeria	316	Algeria	201.0	Algeria	0.0	Algeria	6.0	Algeria	0.0	Algeria	5.0	Algeria	13,00,00,00,000	Algeria	6,26,00,00,000	Algeria	157	Algeria	13,06,000	Algeria	3,25,000	Algeria	12,20,00,00,000	Algeria	2,07,41,263	Algeria	4,16,57,488	Algeria	23,81,741	Algeria	998	Algeria	0	Algeria	6734.0
3	Angola	0.8379	Angola	295	Angola	72	Angola	18	Angola	30	Angola	47	Angola	126	Angola	15	Angola	379	Angola	595	Angola	28	Angola	357	Angola	115	Angola	57.0	Angola	0.0	Angola	0.0	Angola	0.0	Angola	0.0	Angola	7,00,00,00,000	Angola	42,08,00,00,000	Angola	176	Angola	16,66,000	Angola	82300	Angola	8,27,30,00,000	Angola	62,44,205	Angola	3,03,55,880	Angola	12,46,700	Angola	1600	Angola	1300	Angola	5369.0
4	Argentina	0.6521	Argentina	227	Argentina	24	Argentina	7	Argentina	9	Argentina	64	Argentina	100	Argentina	0	Argentina	370	Argentina	739	Argentina	83	Argentina	172	Argentina	26	Argentina	49.0	Argentina	0.0	Argentina	2.0	Argentina	4.0	Argentina	0.0	Argentina	4,20,00,00,000	Argentina	2,14,90,00,00,000	Argentina	1138	Argentina	4,79,400	Argentina	6,85,000	Argentina	2,18,50,00,000	Argentina	2,01,79,430	Argentina	4,46,94,198	Argentina	27,80,400	Argentina	4989	Argentina	11000	Argentina	11968.0

	2020 ranking_Military Strength	2020 ranking_Military Strength Power Index	Airforce Strength_Aircraft Strength	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength	Navy Strength_Submarine Fleet Strength	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget	Finances_defense spending budget value	Finances_External Debt	Finances_External Debt value	Logistics_Airport Totals	Logistics_Airport Totals value	Natural resources_Oil Production	Natural resources_Oil Production value	Natural resources_Oil Consumption	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower	Manpower_Available Manpower value	Manpower_Total Population	Manpower_Total Population value	Geography_Total Square Land Area	Geography_Total Square Land Area value	Geography_Total Coastline Coverage	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage	Geography_Total Waterway Coverage value	Geography_Total Border Coverage	Geography_Total Border Coverage value
0	Afghanistan	1.3444	Afghanistan	260	Afghanistan	0	Afghanistan	25	Afghanistan	30	Afghanistan	0	Afghanistan	187	Afghanistan	0	Afghanistan	0	Afghanistan	1062	Afghanistan	0	Afghanistan	176	Afghanistan	50	Afghanistan	0.0	Afghanistan	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	0.0	Afghanistan	12,00,00,00,000	Afghanistan	2,84,00,00,000	Afghanistan	43	Afghanistan	0	Afghanistan	5500	Afghanistan	0	Afghanistan	1,43,25,743	Afghanistan	3,49,40,837	Afghanistan	6,52,230	Afghanistan	0	Afghanistan	1200	Afghanistan	5987.0
1	Albania	2.3137	Albania	19	Albania	0	Albania	0	Albania	0	Albania	0	Albania	19	Albania	0	Albania	0	Albania	467	Albania	0	Albania	0	Albania	0	Albania	38.0	Albania	Albania	0.0	Albania	0.0	Albania	0.0	Albania	25,00,00,000	Albania	9,50,50,00,000	Albania	4	Albania	16000	Albania	42500	Albania	16,83,00,000	Albania	15,19,438	Albania	30,57,220	Albania	28748	Albania	362	Albania	41	Albania	691.0
2	Algeria	0.4659	Algeria	551	Algeria	103	Algeria	22	Algeria	59	Algeria	87	Algeria	257	Algeria	45	Algeria	880	Algeria	7361	Algeria	320	Algeria	240	Algeria	316	Algeria	201.0	Algeria	Algeria	6.0	Algeria	0.0	Algeria	5.0	Algeria	13,00,00,00,000	Algeria	6,26,00,00,000	Algeria	157	Algeria	13,06,000	Algeria	3,25,000	Algeria	12,20,00,00,000	Algeria	2,07,41,263	Algeria	4,16,57,488	Algeria	23,81,741	Algeria	998	Algeria	0	Algeria	6734.0
3	Angola	0.8379	Angola	295	Angola	72	Angola	18	Angola	30	Angola	47	Angola	126	Angola	15	Angola	379	Angola	595	Angola	28	Angola	357	Angola	115	Angola	57.0	Angola	Angola	0.0	Angola	0.0	Angola	0.0	Angola	7,00,00,00,000	Angola	42,08,00,00,000	Angola	176	Angola	16,66,000	Angola	82300	Angola	8,27,30,00,000	Angola	62,44,205	Angola	3,03,55,880	Angola	12,46,700	Angola	1600	Angola	1300	Angola	5369.0
4	Argentina	0.6521	Argentina	227	Argentina	24	Argentina	7	Argentina	9	Argentina	64	Argentina	100	Argentina	0	Argentina	370	Argentina	739	Argentina	83	Argentina	172	Argentina	26	Argentina	49.0	Argentina	Argentina	2.0	Argentina	4.0	Argentina	0.0	Argentina	4,20,00,00,000	Argentina	2,14,90,00,00,000	Argentina	1138	Argentina	4,79,400	Argentina	6,85,000	Argentina	2,18,50,00,000	Argentina	2,01,79,430	Argentina	4,46,94,198	Argentina	27,80,400	Argentina	4989	Argentina	11000	Argentina	11968.0

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	Geography_Total Square Land Area value	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage value	Geography_Total Border Coverage value
0	Afghanistan	1.3444	260	0	25	30	0	187	0	0	1062	0	176	50	0.0	0.0	0.0	0.0	12,00,00,00,000	2,84,00,00,000	43	0	5500	0	1,43,25,743	3,49,40,837	6,52,230	0	1200	5987.0
1	Albania	2.3137	19	0	0	0	0	19	0	0	467	0	0	0	38.0	0.0	0.0	0.0	25,00,00,000	9,50,50,00,000	4	16000	42500	16,83,00,000	15,19,438	30,57,220	28748	362	41	691.0
2	Algeria	0.4659	551	103	22	59	87	257	45	880	7361	320	240	316	201.0	6.0	0.0	5.0	13,00,00,00,000	6,26,00,00,000	157	13,06,000	3,25,000	12,20,00,00,000	2,07,41,263	4,16,57,488	23,81,741	998	0	6734.0
3	Angola	0.8379	295	72	18	30	47	126	15	379	595	28	357	115	57.0	0.0	0.0	0.0	7,00,00,00,000	42,08,00,00,000	176	16,66,000	82300	8,27,30,00,000	62,44,205	3,03,55,880	12,46,700	1600	1300	5369.0
4	Argentina	0.6521	227	24	7	9	64	100	0	370	739	83	172	26	49.0	2.0	4.0	0.0	4,20,00,00,000	2,14,90,00,00,000	1138	4,79,400	6,85,000	2,18,50,00,000	2,01,79,430	4,46,94,198	27,80,400	4989	11000	11968.0

	country	Finances_defense spending budget value	Finances_External Debt value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	Geography_Total Square Land Area value	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage value
0	Afghanistan	12,00,00,00,000	2,84,00,00,000	0	5500	0	1,43,25,743	3,49,40,837	6,52,230	0	1200
1	Albania	25,00,00,000	9,50,50,00,000	16000	42500	16,83,00,000	15,19,438	30,57,220	28748	362	41
2	Algeria	13,00,00,00,000	6,26,00,00,000	13,06,000	3,25,000	12,20,00,00,000	2,07,41,263	4,16,57,488	23,81,741	998	0
3	Angola	7,00,00,00,000	42,08,00,00,000	16,66,000	82300	8,27,30,00,000	62,44,205	3,03,55,880	12,46,700	1600	1300
4	Argentina	4,20,00,00,000	2,14,90,00,00,000	4,79,400	6,85,000	2,18,50,00,000	2,01,79,430	4,46,94,198	27,80,400	4989	11000
5	Armenia	1,38,50,00,000	10,41,00,00,000	0	47500	0	16,96,085	30,38,217	29743	0	0
6	Australia	26,30,00,00,000	17,14,00,00,00,000	2,63,000	10,05,000	1,82,10,00,000	1,08,08,002	2,34,70,145	77,41,220	25760	2000
7	Austria	3,38,00,00,000	6,30,80,00,00,000	14260	2,15,000	4,30,00,000	40,17,691	87,93,370	83871	0	0
8	Azerbaijan	2,80,50,00,000	17,41,00,00,000	7,89,300	1,80,000	7,00,00,00,000	50,42,346	1,00,46,516	86600	713	0
9	Bahrain	1,41,50,00,000	52,15,00,00,000	45000	55000	12,46,00,000	8,06,505	14,22,659	760	161	0
10	Bangladesh	3,80,00,00,000	50,26,00,00,000	3666	1,12,000	2,80,00,000	7,89,13,289	15,94,53,001	1,43,998	580	8370
11	Belarus	62,37,02,800	39,92,00,00,000	32000	1,90,000	19,80,00,000	49,82,905	95,27,543	2,07,600	0	2500
12	Belgium	4,92,10,00,000	12,81,00,00,00,000	0	6,45,000	0	48,33,107	1,15,70,762	30528	67	2043
13	Bhutan	2,51,20,000	2,67,10,00,000	0	2000	0	1,76,808	7,66,397	38394	0	0
14	Bolivia	66,00,00,000	12,81,00,00,000	59330	60000	21,15,00,000	50,75,416	1,13,06,341	10,98,581	0	10000
15	Bosnia and Herzegovina	16,50,00,000	10,87,00,00,000	0	32500	0	23,94,632	38,49,891	51197	20	0
16	Botswana	45,00,00,000	2,18,70,00,000	0	16500	0	9,62,617	22,49,104	5,81,730	0	0
17	Brazil	27,80,00,00,000	5,47,40,00,00,000	26,22,000	30,20,000	13,00,00,00,000	10,77,64,996	20,88,46,892	85,14,877	7491	50000
18	Bulgaria	1,07,90,00,000	42,06,00,00,000	1000	1,15,000	1,50,00,000	32,78,916	70,57,504	1,10,880	354	470
19	Burkina Faso	13,00,00,000	3,05,60,00,000	0	10500	0	78,00,000	1,97,42,715	2,74,200	0	0
20	Cambodia	60,40,00,000	11,87,00,00,000	0	42500	0	81,20,963	1,64,49,519	1,81,035	443	3700
21	Cameroon	34,70,00,000	9,37,50,00,000	75720	32500	20,00,00,000	94,87,157	2,56,40,965	4,75,440	400	0
22	Canada	22,50,00,00,000	16,08,00,00,00,000	39,77,000	25,00,000	1,69,70,00,00,000	1,61,10,865	3,58,81,659	99,84,670	2,02,080	636
23	Central African Republic	2,00,00,000	77,99,00,000	0	4500	0	23,48,581	57,45,062	6,22,984	0	2800
24	Chad	20,00,00,000	1,72,40,00,000	1,28,000	1,00,000	1,50,00,00,000	60,30,834	1,58,33,116	12,84,000	0	0
25	Chile	4,25,00,00,000	1,83,40,00,00,000	3244	3,25,000	15,00,00,000	86,57,902	1,79,25,262	7,56,102	6435	12290
26	China	2,37,00,00,00,000	15,98,00,00,00,000	38,38,000	1,35,00,000	25,62,00,00,000	75,28,55,402	1,38,46,88,986	95,96,961	14500	1,10,000
27	Colombia	10,60,00,00,000	1,24,60,00,00,000	8,53,600	3,00,000	2,00,20,00,000	2,40,84,498	4,81,68,996	11,38,910	3208	24725
28	Croatia	80,00,00,000	48,10,00,00,000	14000	82000	7,10,00,000	20,88,265	42,70,480	56594	5835	785
29	Cuba	50,00,00,000	30,06,00,00,000	50000	1,52,000	12,40,00,000	59,80,621	1,11,16,396	1,10,860	3735	240
30	Czechia	2,96,90,00,000	2,05,20,00,00,000	2000	2,00,000	1,50,00,000	50,11,174	1,06,86,269	78867	0	664
31	Democratic Republic of the Congo	10,00,00,000	4,96,30,00,000	19160	10500	18,00,00,000	3,41,12,410	8,52,81,024	23,44,858	37	15000
32	Denmark	4,76,00,00,000	4,84,80,00,00,000	1,37,100	1,62,000	49,06,00,000	25,90,457	58,09,502	43094	7314	400
33	Dominican Republic	76,00,00,000	29,16,00,00,000	0	1,25,000	0	48,19,818	1,02,98,756	48320	1288	350
34	Ecuador	2,50,00,00,000	39,29,00,00,000	5,31,300	2,82,000	8,27,30,00,000	76,96,551	1,64,98,502	2,83,560	2237	1500
35	Egypt	11,20,00,00,000	77,47,00,00,000	5,89,400	8,32,000	4,40,00,00,000	4,29,46,553	9,94,13,317	10,01,450	2450	3500
36	El Salvador	16,70,00,000	15,51,00,00,000	0	45600	0	31,06,010	61,87,271	21041	307	0
37	Estonia	68,50,00,000	19,05,00,00,000	0	27500	0	5,96,014	12,44,288	45228	3794	335
38	Ethiopia	35,00,00,000	26,05,00,00,000	0	55000	4,28,000	4,11,43,474	10,83,86,391	11,04,300	0	1,04,300
39	Finland	3,57,00,00,000	1,50,60,00,00,000	0	2,05,000	0	23,07,420	55,37,364	3,38,145	1250	7842
40	France	41,50,00,00,000	53,60,00,00,00,000	15170	16,00,000	7,23,50,000	3,01,11,868	6,73,64,357	6,43,801	4853	8501
41	Gabon	8,30,00,000	6,49,00,00,000	1,98,800	16500	2,00,00,00,000	8,42,741	21,19,036	2,67,667	885	1600
42	Georgia	32,70,00,000	16,99,00,00,000	400	18500	3,50,00,000	22,49,744	49,26,087	69700	310	4349
43	Germany	50,00,00,00,000	53,26,00,00,00,000	43410	25,00,000	14,54,00,000	3,70,10,559	8,04,57,737	3,57,022	2389	7467
44	Ghana	71,00,00,000	22,14,00,00,000	1,51,000	65000	66,00,00,000	1,25,61,805	2,81,02,471	2,38,533	539	1293
45	Greece	4,84,40,00,000	5,06,60,00,00,000	2753	3,50,000	1,00,00,000	49,93,347	1,07,61,523	1,31,957	13676	6
46	Guatemala	24,00,00,000	22,92,00,00,000	9666	85000	8,30,70,000	70,63,622	1,65,81,273	1,08,889	400	990
47	Honduras	20,50,00,000	8,62,50,00,000	0	60000	0	41,04,696	91,82,766	1,12,090	832	465
48	Hungary	2,08,00,00,000	1,38,10,00,00,000	14000	1,45,000	2,51,00,000	46,18,081	98,25,704	1,03,000	0	1622
49	India	61,00,00,00,000	5,01,60,00,00,000	7,33,900	50,00,000	4,62,10,00,000	62,24,80,340	1,29,68,34,042	32,87,263	7000	14500
50	Indonesia	7,60,00,00,000	3,44,40,00,00,000	8,01,700	16,30,000	3,23,00,00,000	13,08,68,127	26,27,87,403	19,04,569	54716	21579
51	Iran	19,60,00,00,000	7,99,50,00,000	44,69,000	20,00,000	1,58,40,00,00,000	4,73,24,105	8,30,24,745	16,48,195	2440	850
52	Iraq	1,73,00,00,000	73,02,00,00,000	44,54,000	8,25,000	1,42,50,00,00,000	1,63,99,240	4,01,94,216	4,38,317	58	5279
53	Ireland	87,00,00,000	24,70,00,00,00,000	0	1,45,000	0	23,66,779	50,68,050	70273	1448	956
54	Israel	20,00,00,00,000	88,66,00,00,000	390	2,50,000	1,27,30,000	36,47,983	84,24,904	20770	273	0
55	Italy	27,80,00,00,000	24,44,00,00,00,000	77530	13,00,000	55,67,00,000	2,80,11,003	6,22,46,674	3,01,340	7600	2400
56	Ivory Coast	55,00,00,000	13,07,00,00,000	54000	25600	10,00,00,000	1,11,60,747	2,62,60,582	3,22,463	515	980
57	Japan	49,00,00,00,000	32,40,00,00,00,000	3841	40,00,000	4,41,20,000	5,38,73,803	12,61,68,156	3,77,915	29751	1770
58	Jordan	2,60,00,00,000	29,34,00,00,000	22	1,10,000	10,00,000	33,46,692	1,04,58,413	89342	26	540
59	Kazakhstan	4,00,00,00,000	1,67,50,00,00,000	17,77,000	2,45,000	30,00,00,00,000	85,85,003	1,87,44,548	27,24,900	26	0
60	Kenya	12,10,00,000	27,59,00,00,000	0	82000	0	1,96,00,998	4,83,97,527	5,80,367	536	0
61	Kuwait	6,83,10,00,000	47,24,00,00,000	27,53,000	3,45,000	1,01,50,00,00,000	16,41,971	29,16,467	17818	499	0
62	Kyrgyzstan	2,00,00,000	8,16,40,00,000	1000	17200	4,00,00,000	30,29,935	58,49,296	1,99,951	0	600
63	Laos	1,85,00,000	14,90,00,00,000	0	4500	0	32,48,143	72,34,171	2,36,800	0	4600
64	Latvia	72,40,00,000	40,02,00,00,000	0	32500	0	8,40,595	19,23,559	64589	498	300
65	Lebanon	2,50,00,00,000	39,30,00,00,000	0	1,10,000	0	21,35,026	61,00,075	10400	225	170
66	Liberia	1,30,00,000	1,03,60,00,000	0	4500	0	22,26,923	48,09,768	1,11,369	579	0
67	Libya	3,00,00,00,000	3,02,00,00,000	8,97,100	3,15,000	48,36,00,00,000	35,52,871	67,54,507	17,59,540	1770	0
68	Lithuania	1,10,70,00,000	34,48,00,00,000	2000	72400	1,20,00,000	17,79,322	27,93,284	65300	90	441
69	Madagascar	11,50,00,000	4,08,90,00,000	0	18500	0	1,01,45,026	2,56,83,610	5,87,040	4828	600
70	Malaysia	4,00,00,00,000	2,17,20,00,00,000	6,47,900	8,35,000	3,60,00,00,000	1,52,01,837	3,18,09,660	3,29,847	4675	7200
71	Mali	7,00,00,000	4,19,20,00,000	0	5500	0	59,91,557	1,84,29,893	12,40,192	0	0
72	Mauritania	5,02,50,000	4,15,00,00,000	4000	18500	2,00,00,000	15,55,374	38,40,429	10,30,700	754	0
73	Mexico	7,00,00,00,000	4,45,80,00,00,000	19,81,000	20,00,000	7,64,00,00,000	6,04,60,418	12,59,59,205	19,64,375	9330	2900
74	Moldova	3,00,00,000	6,54,90,00,000	0	16320	0	20,14,503	34,37,720	33851	0	558
75	Mongolia	15,50,00,000	25,33,00,00,000	20000	25000	0	18,18,609	31,03,428	15,64,116	0	580
76	Montenegro	6,50,00,000	2,51,60,00,000	0	4860	0	1,46,191	6,14,249	13812	294	0
77	Morocco	10,00,00,00,000	51,48,00,00,000	160	2,06,000	6,84,000	1,71,57,065	3,43,14,130	4,46,550	1835	0
78	Mozambique	24,50,00,000	10,91,00,00,000	0	22500	0	46,29,744	2,72,33,789	7,99,380	2470	460
79	Myanmar	2,65,00,00,000	6,59,40,00,000	12000	42600	13,90,00,000	3,02,58,643	5,56,22,506	6,76,578	1930	12800
80	Namibia	50,50,00,000	7,96,90,00,000	0	25000	0	5,82,642	25,33,224	8,24,292	1572	0
81	Nepal	21,30,00,000	5,84,90,00,000	0	20000	0	1,47,39,923	2,97,17,587	1,47,181	0	0
82	Netherlands	12,41,90,00,000	40,63,00,00,00,000	18170	8,40,000	11,32,00,000	78,20,960	1,71,51,228	41543	451	6237
83	New Zealand	4,30,00,00,000	91,62,00,00,000	31480	1,50,000	5,69,00,000	21,15,989	45,45,627	2,67,710	15134	0
84	Nicaragua	14,00,00,000	11,31,00,00,000	0	35000	0	30,30,436	60,85,213	1,30,370	910	2220
85	Niger	32,50,00,000	3,72,80,00,000	11000	6500	15,00,00,000	67,54,519	1,98,66,231	12,67,000	0	300
86	Nigeria	2,15,50,00,000	40,96,00,00,000	19,46,000	2,75,000	37,06,00,00,000	7,71,08,499	20,34,52,505	9,23,768	853	8600
87	North Korea	1,60,00,00,000	5,00,00,00,000	0	15500	0	1,30,45,878	2,53,81,085	1,20,538	2495	2250
88	North Macedonia	10,81,52,512	8,79,00,00,000	0	18500	0	10,53,116	21,18,945	25713	0	280
89	Norway	7,17,90,00,000	6,42,30,00,00,000	16,18,000	2,60,000	6,61,10,00,000	21,70,365	53,72,191	3,23,802	25148	1577
90	Oman	8,68,60,00,000	46,27,00,00,000	9,70,400	1,00,000	5,37,30,00,000	17,64,529	34,94,116	3,09,500	2092	0
91	Pakistan	11,40,00,00,000	82,19,00,00,000	89720	5,75,000	35,06,00,000	9,63,44,277	20,78,62,518	7,96,095	1046	25220
92	Panama	50,00,00,000	91,53,00,00,000	0	1,00,000	0	17,59,698	38,00,644	75420	2490	800
93	Paraguay	25,00,00,000	17,70,00,00,000	0	30000	0	33,72,366	70,25,763	4,06,752	0	3100
94	Peru	2,56,00,00,000	66,25,00,00,000	43520	2,10,000	47,30,00,000	1,53,52,302	3,13,31,228	12,85,216	2414	8808
95	Philippines	3,47,00,00,000	76,18,00,00,000	15000	3,20,000	13,85,00,000	5,18,87,757	10,58,93,381	3,00,000	36289	3219
96	Poland	12,00,00,00,000	2,41,00,00,00,000	20070	5,85,000	13,78,00,000	1,88,26,137	3,84,20,687	3,12,685	440	3997
97	Portugal	3,35,80,00,000	4,49,00,00,00,000	0	2,65,000	0	48,04,949	1,03,55,493	92090	1793	210
98	Qatar	6,00,00,00,000	1,67,80,00,00,000	15,00,000	1,90,000	25,24,00,00,000	6,12,164	23,63,569	11586	563	0
99	Republic of the Congo	71,50,00,000	4,60,50,00,000	2,44,200	1,85,000	1,60,00,00,000	19,38,754	50,62,021	3,42,000	169	1120
100	Romania	5,05,00,00,000	95,97,00,00,000	73740	2,40,000	60,00,00,000	1,10,07,501	2,14,57,116	2,38,391	225	1731
101	Russia	48,00,00,00,000	5,39,60,00,00,000	1,05,80,000	32,25,000	80,00,00,00,000	6,96,40,160	14,21,22,776	1,70,98,242	37653	1,02,000
102	Saudi Arabia	67,60,00,00,000	2,05,10,00,00,000	1,01,30,000	40,00,000	2,66,50,00,00,000	1,77,03,745	3,30,91,113	21,49,690	2640	0
103	Serbia	90,70,00,000	29,50,00,00,000	18740	75000	7,75,00,000	34,68,274	70,78,110	77474	0	587
104	Sierra Leone	7,55,00,000	1,61,50,00,000	0	9500	0	14,32,872	63,12,212	71740	402	800
105	Singapore	11,20,00,00,000	5,66,10,00,00,000	0	13,40,000	0	40,17,314	59,95,991	697	193	10
106	Slovakia	2,12,18,17,575	75,04,00,00,000	200	82000	90,00,000	27,76,970	54,45,040	49035	0	172
107	Slovenia	58,10,00,000	46,30,00,00,000	5	55000	0	10,02,714	21,02,126	20273	47	0
108	Somalia	6,22,00,000	5,30,00,00,000	0	6200	0	27,02,167	1,12,59,029	6,37,657	3025	0
109	South Africa	4,27,80,00,000	1,56,30,00,00,000	2000	6,05,000	1,50,00,000	2,62,50,220	5,53,80,210	12,19,090	2798	0
110	South Korea	44,00,00,00,000	3,84,60,00,00,000	0	28,00,000	0	2,57,09,049	5,14,18,097	99720	2413	1600
111	South Sudan	8,00,00,000	2,25,00,00,000	1,50,200	60000	3,75,00,00,000	52,45,155	1,02,04,581	6,44,329	0	0
112	Spain	15,10,00,00,000	20,94,00,00,00,000	2252	12,26,000	15,00,00,000	2,31,36,275	4,93,31,076	5,05,370	4964	1000
113	Sri Lanka	2,50,00,00,000	51,72,00,00,000	0	90000	0	1,09,27,071	2,25,76,592	65610	1340	160
114	Sudan	2,47,00,00,000	56,05,00,00,000	1,02,300	96800	5,00,00,00,000	2,40,61,430	4,31,20,843	18,61,484	853	1723
115	Suriname	6,30,00,000	1,70,00,00,000	17000	15100	8,39,80,000	1,32,740	5,97,927	1,63,820	386	1200
116	Sweden	6,32,69,23,600	9,39,90,00,00,000	0	3,20,000	0	41,16,808	1,00,40,995	4,50,295	3218	2052
117	Switzerland	5,00,00,00,000	16,64,00,00,00,000	0	2,60,000	0	36,32,250	82,92,809	41277	0	1299
118	Syria	1,80,00,00,000	4,98,90,00,000	14000	2,60,000	2,50,00,00,000	1,24,50,728	1,94,54,263	1,85,180	193	900
119	Taiwan	10,72,50,00,000	1,81,90,00,00,000	196	10,32,000	23,80,000	1,21,96,809	2,35,45,963	35980	1566	3720
120	Tajikistan	1,60,00,00,000	5,75,00,00,000	180	20500	1,20,00,000	40,95,924	86,04,882	1,43,100	0	200
121	Tanzania	22,30,00,000	17,66,00,00,000	0	46000	0	1,02,58,498	5,54,51,343	9,47,300	1424	0
122	Thailand	7,10,00,00,000	1,32,00,00,00,000	2,39,700	13,45,000	39,64,00,000	3,55,43,014	6,86,15,858	5,13,120	3219	4000
123	Tunisia	55,00,00,000	30,19,00,00,000	49170	90500	42,50,00,000	58,50,224	1,15,16,189	1,63,610	1148	0
124	Turkey	19,00,00,00,000	4,52,40,00,00,000	2,45,000	8,45,000	38,85,00,000	4,18,47,478	8,12,57,239	7,83,562	7200	1200
125	Turkmenistan	20,00,00,000	53,94,00,000	2,30,880	1,50,000	60,00,00,000	27,97,493	54,11,012	4,88,100	1768	1300
126	Uganda	93,50,00,000	10,80,00,00,000	0	17500	2,50,00,00,000	1,47,07,350	4,08,53,749	2,41,038	0	0
127	Ukraine	5,40,00,00,000	1,30,00,00,00,000	29650	3,25,000	39,50,00,000	2,21,95,911	4,39,52,299	6,03,550	2782	2150
128	United Arab Emirates	22,75,50,00,000	2,37,60,00,00,000	31,74,000	5,75,000	97,80,00,00,000	58,20,789	97,01,315	83600	1318	0
129	United Kingdom	55,10,00,00,000	81,26,00,00,00,000	9,10,500	16,00,000	2,56,40,00,000	2,99,48,413	6,51,05,246	2,43,610	12429	3200
130	United States	7,50,00,00,00,000	1,79,10,00,00,00,000	93,52,000	2,00,00,000	36,52,00,00,000	14,48,72,845	32,92,56,465	98,26,675	19924	41009
131	Uruguay	4,95,00,00,000	28,37,00,00,000	0	55600	0	15,49,878	33,69,299	1,76,215	660	1600
132	Uzbekistan	97,50,00,000	16,90,00,00,000	46070	1,42,000	59,40,00,000	1,59,12,566	3,00,23,709	4,47,400	420	1100
133	Venezuela	74,50,00,000	1,00,30,00,00,000	20,07,000	5,75,000	3,00,90,00,00,000	1,42,60,129	3,16,89,176	9,12,050	2800	7100
134	Vietnam	5,50,00,00,000	96,58,00,00,000	2,71,400	5,25,000	4,40,00,00,000	5,10,43,216	9,70,40,334	3,31,210	3444	17702
135	Yemen	1,40,00,00,000	7,06,80,00,000	12260	1,45,000	3,00,00,00,000	1,12,66,221	2,86,67,230	5,27,968	1906	0
136	Zambia	4,00,00,000	11,66,00,00,000	0	21000	0	61,66,905	1,64,45,079	7,52,618	0	2250
137	Zimbabwe	10,00,00,000	9,35,70,00,000	0	19500	0	55,84,086	1,40,30,368	3,90,757	0	0

	count	mean	std	min	25%	50%	75%	max
Military Strength Power Index	138.0	1.460716e+00	1.324018e+00	6.060000e-02	5.748000e-01	1.034100e+00	2.021525e+00	1.016810e+01
Airforce Strength_Aircraft Strength value	138.0	3.884710e+02	1.231982e+03	0.000000e+00	3.300000e+01	1.110000e+02	2.902500e+02	1.326400e+04
Airforce Strength_Fighter/Interceptor Strength value	138.0	8.156522e+01	2.303248e+02	0.000000e+00	0.000000e+00	1.700000e+01	6.050000e+01	2.085000e+03
Airforce Strength_Attack Aircraft Strength value	138.0	2.576087e+01	9.452822e+01	0.000000e+00	0.000000e+00	0.000000e+00	1.575000e+01	7.420000e+02
Airforce Strength_Transport Aircraft Fleet Strength value	138.0	3.023188e+01	9.233044e+01	0.000000e+00	3.000000e+00	9.000000e+00	2.600000e+01	9.450000e+02
Airforce Strength_Trainer Aircraft Fleet value	138.0	8.283333e+01	2.408037e+02	0.000000e+00	4.250000e+00	2.600000e+01	7.925000e+01	2.643000e+03
Airforce Strength_Helicopter Fleet Strength value	138.0	1.540652e+02	5.201836e+02	0.000000e+00	1.525000e+01	4.400000e+01	1.267500e+02	5.768000e+03
Airforce Strength_Attack Helicopter Fleet Strength value	138.0	2.562319e+01	9.732609e+01	0.000000e+00	0.000000e+00	2.000000e+00	1.775000e+01	9.670000e+02
Land Strength_Tank Strength value	138.0	6.465652e+02	1.515464e+03	0.000000e+00	1.925000e+01	1.530000e+02	4.362500e+02	1.295000e+04
Land Strength_AFV/APC Strength value	138.0	2.485696e+03	5.410546e+03	0.000000e+00	2.502500e+02	7.345000e+02	2.000000e+03	3.925300e+04
Land Strength_Self-Propelled Artillery Strength value	138.0	2.121594e+02	6.880290e+02	0.000000e+00	0.000000e+00	2.300000e+01	9.725000e+01	6.083000e+03
Land Strength_Towed Artillery Strength value	138.0	3.939783e+02	8.057817e+02	0.000000e+00	2.425000e+01	1.080000e+02	2.965000e+02	4.465000e+03
Land Strength_Rocket Projector Strength value	138.0	1.569348e+02	4.843810e+02	0.000000e+00	0.000000e+00	2.400000e+01	9.975000e+01	3.860000e+03
Navy Strength_Navy Fleet Strengths value	138.0	7.436232e+01	1.410382e+02	0.000000e+00	3.000000e+00	2.750000e+01	6.800000e+01	9.840000e+02
Navy Strength_Aircraft Carrier Fleet Strength value	138.0	3.043478e-01	1.798656e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.000000e+01
Navy Strength_Submarine Fleet Strength value	138.0	3.978261e+00	1.260313e+01	0.000000e+00	0.000000e+00	0.000000e+00	2.000000e+00	8.300000e+01
Navy Strength_Destroyer Fleet Strength value	138.0	1.710145e+00	9.154954e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	9.100000e+01
Navy Strength_Frigate Fleet Strength value	138.0	2.891304e+00	5.969123e+00	0.000000e+00	0.000000e+00	0.000000e+00	4.000000e+00	5.200000e+01
Finances_defense spending budget value	138.0	1.399363e+10	6.731189e+10	1.300000e+07	3.255000e+08	2.100909e+09	6.957750e+09	7.500000e+11
Finances_External Debt value	138.0	5.194983e+11	1.847252e+12	5.394000e+08	9.361500e+09	3.997000e+10	1.830250e+11	1.791000e+13
Logistics_Airport Totals value	138.0	2.900435e+02	1.212967e+03	2.000000e+00	3.350000e+01	6.800000e+01	1.507500e+02	1.351300e+04
Natural resources_Oil Production value	138.0	5.855524e+05	1.667139e+06	0.000000e+00	0.000000e+00	1.313000e+04	2.228600e+05	1.058000e+07
Natural resources_Oil Consumption value	138.0	7.020187e+05	2.168810e+06	2.000000e+03	3.250000e+04	1.335000e+05	3.487500e+05	2.000000e+07
Natural resources_Proven Oil Reserves value	138.0	1.194412e+10	4.275090e+10	0.000000e+00	0.000000e+00	8.028500e+07	2.001500e+09	3.009000e+11
Manpower_Available Manpower value	138.0	2.544297e+07	8.450698e+07	1.327400e+05	2.618384e+06	5.986089e+06	1.756708e+07	7.528554e+08
Manpower_Total Population value	138.0	5.318058e+07	1.651090e+08	5.979270e+05	5.819450e+06	1.613910e+07	4.068887e+07	1.384689e+09

Military Power Clustering Project¶

PREFACE¶

Roadmap For The Data¶

TASKS¶

DATA SET¶

Import Libraries¶

Ingest Data¶

EDA¶

Implement basic steps to see how is your data looks like¶

Convert multi index columns to one level¶

Check for missing values¶

Deal with missing values¶

Fix the dtypes¶

Drop the features that not affect military power¶

Visualy inspect the some features¶

Decriptive statistics¶

Data Preprocessing¶

Modelling¶

Hopkins Test¶

Clustering with K-means¶

Elbow Method¶

We use this method to find optimal k value by looking up to break point of graph.¶

Silhouette analysis¶

Model Building and label visualisation¶

Evaluate¶

Results¶

Hierarchical Clustering¶

Dendogram¶

Model Building and label visualisation¶

Results¶

REFERENCES¶

	variable	missing
0	country	False
1	country	False
2	country	False
3	country	False
4	country	False
...	...	...
4273	Geography_Total Border Coverage value	False
4274	Geography_Total Border Coverage value	False
4275	Geography_Total Border Coverage value	False
4276	Geography_Total Border Coverage value	False
4277	Geography_Total Border Coverage value	False

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	Geography_Total Square Land Area value	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage value	Geography_Total Border Coverage value
5	Armenia	2.1251	64	0	9	3	13	37	20	110	748	38	150	68	0.0	NaN	NaN	NaN	NaN	1,38,50,00,000	10,41,00,00,000	11	0	47500	0	16,96,085	30,38,217	29743	NaN	0	1570.0
7	Austria	0.9568	120	15	0	11	32	62	0	56	467	33	0	0	0.0	NaN	NaN	NaN	NaN	3,38,00,00,000	6,30,80,00,00,000	52	14260	2,15,000	4,30,00,000	40,17,691	87,93,370	83871	NaN	0	2524.0
11	Belarus	0.8179	202	39	68	4	28	63	21	532	1560	482	228	238	0.0	NaN	NaN	NaN	NaN	62,37,02,800	39,92,00,00,000	65	32000	1,90,000	19,80,00,000	49,82,905	95,27,543	2,07,600	NaN	2500	3599.0
13	Bhutan	10.1681	2	0	0	0	0	2	0	0	27	0	0	0	0.0	NaN	NaN	NaN	NaN	2,51,20,000	2,67,10,00,000	2	0	2000	0	1,76,808	7,66,397	38394	NaN	0	1136.0
14	Bolivia	0.9942	69	0	0	14	23	38	0	54	137	0	86	0	173.0	0.0	0.0	0.0	0.0	66,00,00,000	12,81,00,00,000	855	59330	60000	21,15,00,000	50,75,416	1,13,06,341	10,98,581	NaN	10000	7252.0
16	Botswana	2.0582	45	10	0	11	8	16	0	55	245	0	30	20	NaN	NaN	NaN	NaN	NaN	45,00,00,000	2,18,70,00,000	74	0	16500	0	9,62,617	22,49,104	5,81,730	NaN	0	4347.0
19	Burkina Faso	1.9009	16	0	3	1	4	8	2	0	350	0	0	0	NaN	NaN	NaN	NaN	NaN	13,00,00,000	3,05,60,00,000	23	0	10500	0	78,00,000	1,97,42,715	2,74,200	NaN	0	3611.0
23	Central African Republic	3.2889	3	0	0	2	0	1	0	4	55	0	20	0	NaN	NaN	NaN	NaN	NaN	2,00,00,000	77,99,00,000	39	0	4500	0	23,48,581	57,45,062	6,22,984	NaN	2800	5920.0
24	Chad	1.6383	35	0	6	6	4	17	3	60	372	10	51	10	NaN	NaN	NaN	NaN	NaN	20,00,00,000	1,72,40,00,000	59	1,28,000	1,00,000	1,50,00,00,000	60,30,834	1,58,33,116	12,84,000	NaN	0	6406.0
30	Czechia	0.5531	95	12	16	8	15	44	15	116	682	86	85	0	NaN	NaN	NaN	NaN	NaN	2,96,90,00,000	2,05,20,00,00,000	128	2000	2,00,000	1,50,00,000	50,11,174	1,06,86,269	78867	NaN	664	2046.0
38	Ethiopia	0.8581	86	24	0	9	20	33	8	400	114	67	650	183	NaN	NaN	NaN	NaN	NaN	35,00,00,000	26,05,00,00,000	57	0	55000	4,28,000	4,11,43,474	10,83,86,391	11,04,300	NaN	1,04,300	5925.0
48	Hungary	0.8215	47	12	0	5	6	24	8	30	944	0	300	0	NaN	NaN	NaN	NaN	NaN	2,08,00,00,000	1,38,10,00,00,000	41	14000	1,45,000	2,51,00,000	46,18,081	98,25,704	1,03,000	NaN	1622	2106.0
62	Kyrgyzstan	1.9244	5	0	0	0	0	5	2	150	385	30	141	21	NaN	NaN	NaN	NaN	NaN	2,00,00,000	8,16,40,00,000	28	1000	17200	4,00,00,000	30,29,935	58,49,296	1,99,951	NaN	600	4573.0
63	Laos	3.4433	33	0	0	2	4	27	0	65	30	0	62	0	36.0	0.0	0.0	0.0	0.0	1,85,00,000	14,90,00,00,000	41	0	4500	0	32,48,143	72,34,171	2,36,800	NaN	4600	5274.0
71	Mali	1.8941	29	9	0	6	5	8	6	80	248	0	50	32	NaN	NaN	NaN	NaN	NaN	7,00,00,000	4,19,20,00,000	25	0	5500	0	59,91,557	1,84,29,893	12,40,192	NaN	0	7908.0
74	Moldova	2.1291	3	0	0	1	0	2	0	0	341	9	52	11	NaN	NaN	NaN	NaN	NaN	3,00,00,000	6,54,90,00,000	7	0	16320	0	20,14,503	34,37,720	33851	NaN	558	1885.0
75	Mongolia	2.0299	9	0	0	3	0	6	0	470	750	0	300	130	NaN	NaN	NaN	NaN	NaN	15,50,00,000	25,33,00,00,000	44	20000	25000	0	18,18,609	31,03,428	15,64,116	NaN	580	8082.0
81	Nepal	2.9891	16	0	0	3	0	13	0	0	214	0	84	0	NaN	NaN	NaN	NaN	NaN	21,30,00,000	5,84,90,00,000	47	0	20000	0	1,47,39,923	2,97,17,587	1,47,181	NaN	0	3159.0
85	Niger	2.0153	16	0	2	5	0	7	1	0	182	0	0	0	NaN	NaN	NaN	NaN	NaN	32,50,00,000	3,72,80,00,000	30	11000	6500	15,00,00,000	67,54,519	1,98,66,231	12,67,000	NaN	300	5834.0
88	North Macedonia	3.1808	20	0	0	0	10	14	4	31	251	0	144	24	NaN	NaN	NaN	NaN	NaN	10,81,52,512	8,79,00,00,000	10	0	18500	0	10,53,116	21,18,945	25713	NaN	280	838.0
93	Paraguay	2.1898	33	0	0	6	15	11	0	13	63	0	108	0	40.0	0.0	0.0	0.0	0.0	25,00,00,000	17,70,00,00,000	799	0	30000	0	33,72,366	70,25,763	4,06,752	NaN	3100	4655.0
103	Serbia	0.9479	109	9	17	1	38	44	0	293	904	87	144	88	19.0	0.0	0.0	0.0	0.0	90,70,00,000	29,50,00,00,000	26	18740	75000	7,75,00,000	34,68,274	70,78,110	77474	NaN	587	2322.0
106	Slovakia	0.8466	47	10	0	7	8	21	0	20	661	16	9	25	0.0	NaN	NaN	NaN	NaN	2,12,18,17,575	75,04,00,00,000	35	200	82000	90,00,000	27,76,970	54,45,040	49035	NaN	172	1587.0
111	South Sudan	2.3501	19	0	0	2	0	17	6	110	200	24	0	14	0.0	2.0	22.0	12.0	18.0	8,00,00,000	2,25,00,00,000	85	1,50,200	60000	3,75,00,00,000	52,45,155	1,02,04,581	6,44,329	NaN	0	6018.0
117	Switzerland	0.5259	169	54	0	18	54	42	0	326	1338	224	0	0	0.0	NaN	NaN	NaN	NaN	5,00,00,00,000	16,64,00,00,00,000	63	0	2,60,000	0	36,32,250	82,92,809	41277	NaN	1299	1770.0
120	Tajikistan	1.8661	25	0	0	1	4	20	6	253	347	0	90	128	0.0	NaN	NaN	NaN	NaN	1,60,00,00,000	5,75,00,00,000	24	180	20500	1,20,00,000	40,95,924	86,04,882	1,43,100	NaN	200	4130.0
126	Uganda	1.6176	48	10	0	2	10	26	5	371	1081	6	53	26	8.0	0.0	0.0	0.0	0.0	93,50,00,000	10,80,00,00,000	47	0	17500	2,50,00,00,000	1,47,07,350	4,08,53,749	2,41,038	NaN	0	2729.0
136	Zambia	1.6464	108	18	0	11	52	27	0	75	184	0	42	50	0.0	0.0	0.0	0.0	0.0	4,00,00,000	11,66,00,00,000	88	0	21000	0	61,66,905	1,64,45,079	7,52,618	NaN	2250	6043.0
137	Zimbabwe	1.7577	90	10	0	14	38	28	6	42	300	0	65	22	0.0	0.0	0.0	0.0	0.0	10,00,00,000	9,35,70,00,000	196	0	19500	0	55,84,086	1,40,30,368	3,90,757	NaN	0	3229.0

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	Geography_Total Square Land Area value	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage value	Geography_Total Border Coverage value
110	South Korea	0.1509	1649	414	71	41	298	803	112	2614	14000	3040	3854	575	234.0	NaN	NaN	NaN	NaN	44,00,00,00,000	3,84,60,00,00,000	111	0	28,00,000	0	2,57,09,049	5,14,18,097	99720	2413	1600	237.0
132	Uzbekistan	0.8086	185	66	20	18	2	79	25	420	1215	137	0	98	0.0	NaN	NaN	NaN	NaN	97,50,00,000	16,90,00,00,000	53	46070	1,42,000	59,40,00,000	1,59,12,566	3,00,23,709	4,47,400	420	1100	6893.0

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	Geography_Total Square Land Area value	Geography_Total Coastline Coverage value	Geography_Total Waterway Coverage value	Geography_Total Border Coverage value
6	Australia	0.3225	464	82	6	38	174	133	22	59	3051	0	54	0	48.0	2.0	6.0	2.0	8.0	26,30,00,00,000	17,14,00,00,00,000	480	2,63,000	10,05,000	1,82,10,00,000	1,08,08,002	2,34,70,145	77,41,220	25760	2000	NaN
9	Bahrain	1.8547	109	17	0	2	37	65	22	180	843	33	26	17	39.0	0.0	0.0	0.0	1.0	1,41,50,00,000	52,15,00,00,000	4	45000	55000	12,46,00,000	8,06,505	14,22,659	760	161	0	NaN
57	Japan	0.1501	1561	279	12	60	427	637	119	1004	3130	238	500	99	155.0	4.0	20.0	40.0	0.0	49,00,00,00,000	32,40,00,00,00,000	175	3841	40,00,000	4,41,20,000	5,38,73,803	12,61,68,156	3,77,915	29751	1770	NaN
69	Madagascar	3.0869	6	0	0	0	0	6	0	12	103	0	25	0	8.0	0.0	0.0	0.0	0.0	11,50,00,000	4,08,90,00,000	83	0	18500	0	1,01,45,026	2,56,83,610	5,87,040	4828	600	NaN
83	New Zealand	1.3684	48	0	0	7	13	22	0	0	200	0	24	0	11.0	0.0	0.0	0.0	2.0	4,30,00,00,000	91,62,00,00,000	123	31480	1,50,000	5,69,00,000	21,15,989	45,45,627	2,67,710	15134	0	NaN
95	Philippines	0.7852	171	0	19	23	24	97	0	7	513	0	286	0	103.0	0.0	0.0	0.0	2.0	3,47,00,00,000	76,18,00,00,000	247	15000	3,20,000	13,85,00,000	5,18,87,757	10,58,93,381	3,00,000	36289	3219	NaN
105	Singapore	0.7966	235	100	0	9	36	77	17	180	3000	48	77	24	40.0	0.0	0.0	0.0	6.0	11,20,00,00,000	5,66,10,00,00,000	9	0	13,40,000	0	40,17,314	59,95,991	697	193	10	NaN
113	Sri Lanka	1.4661	77	0	0	15	6	54	9	202	570	0	209	22	40.0	0.0	0.0	0.0	0.0	2,50,00,00,000	51,72,00,00,000	19	0	90000	0	1,09,27,071	2,25,76,592	65610	1340	160	NaN
119	Taiwan	0.4008	744	289	0	19	207	210	91	1180	2000	482	1160	115	117.0	0.0	4.0	4.0	22.0	10,72,50,00,000	1,81,90,00,00,000	37	196	10,32,000	23,80,000	1,21,96,809	2,35,45,963	35980	1566	3720	NaN

	0
1	NaN
2	614.980192
3	260.672578
4	161.915966
5	145.089112
6	129.687875
7	98.586879
8	59.573038

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	cluster
26	China	0.0691	3210	1232	371	224	314	911	281	3500	33000	3800	3600	2650	777.0	2.0	74.0	36.0	52.0	2.370000e+11	1.598000e+12	507	3838000.0	13500000.0	2.562000e+10	752855402.0	1.384689e+09	2
101	Russia	0.0681	4163	873	742	424	497	1522	531	12950	27038	6083	4465	3860	603.0	1.0	62.0	16.0	10.0	4.800000e+10	5.396000e+11	1218	10580000.0	3225000.0	8.000000e+10	69640160.0	1.421228e+08	2

cluster	0	1	2
Military Strength Power Index	0.023495	-1.061327	-1.055263
Airforce Strength_Aircraft Strength value	-0.117501	10.489144	2.686763
Airforce Strength_Fighter/Interceptor Strength value	-0.127346	8.729991	4.230860
Airforce Strength_Attack Aircraft Strength value	-0.137689	7.317921	5.635064
Airforce Strength_Transport Aircraft Fleet Strength value	-0.120965	9.943640	3.193295
Airforce Strength_Trainer Aircraft Fleet value	-0.098964	10.670489	1.344839
Airforce Strength_Helicopter Fleet Strength value	-0.110602	10.831533	2.049863
Airforce Strength_Attack Helicopter Fleet Strength value	-0.130020	9.707636	3.922510
Land Strength_Tank Strength value	-0.102035	3.736804	5.018954
Land Strength_AFV/APC Strength value	-0.126185	6.820244	5.107360
Land Strength_Self-Propelled Artillery Strength value	-0.115742	1.827546	6.898793
Land Strength_Towed Artillery Strength value	-0.088785	2.922092	4.531968
Land Strength_Rocket Projector Strength value	-0.113657	2.505197	6.419227
Navy Strength_Navy Fleet Strengths value	-0.086812	2.957722	4.380943
Navy Strength_Aircraft Carrier Fleet Strength value	-0.091292	10.990097	0.667169
Navy Strength_Submarine Fleet Strength value	-0.112117	4.939065	5.098333
Navy Strength_Destroyer Fleet Strength value	-0.111959	9.788704	2.662858
Navy Strength_Frigate Fleet Strength value	-0.066416	-0.486141	4.726171
Finances_defense spending budget value	-0.109676	10.974103	1.916073
Finances_External Debt value	-0.074411	9.448554	0.298445
Logistics_Airport Totals value	-0.088062	10.941042	0.473666
Natural resources_Oil Production value	-0.098166	5.277535	3.987416
Natural resources_Oil Consumption value	-0.118669	8.930373	3.544980
Natural resources_Proven Oil Reserves value	-0.018487	0.576956	0.959389
Manpower_Available Manpower value	-0.078388	1.418403	4.581992
Manpower_Total Population value	-0.076390	1.678174	4.317225

	country	Military Strength Power Index	Airforce Strength_Aircraft Strength value	Airforce Strength_Fighter/Interceptor Strength value	Airforce Strength_Attack Aircraft Strength value	Airforce Strength_Transport Aircraft Fleet Strength value	Airforce Strength_Trainer Aircraft Fleet value	Airforce Strength_Helicopter Fleet Strength value	Airforce Strength_Attack Helicopter Fleet Strength value	Land Strength_Tank Strength value	Land Strength_AFV/APC Strength value	Land Strength_Self-Propelled Artillery Strength value	Land Strength_Towed Artillery Strength value	Land Strength_Rocket Projector Strength value	Navy Strength_Navy Fleet Strengths value	Navy Strength_Aircraft Carrier Fleet Strength value	Navy Strength_Submarine Fleet Strength value	Navy Strength_Destroyer Fleet Strength value	Navy Strength_Frigate Fleet Strength value	Finances_defense spending budget value	Finances_External Debt value	Logistics_Airport Totals value	Natural resources_Oil Production value	Natural resources_Oil Consumption value	Natural resources_Proven Oil Reserves value	Manpower_Available Manpower value	Manpower_Total Population value	cluster_1
2	Algeria	0.4659	551	103	22	59	87	257	45	880	7361	320	240	316	201.0	0.0	6.0	0.0	5.0	1.300000e+10	6.260000e+09	157	1306000.0	325000.0	1.220000e+10	20741263.0	41657488.0	1
6	Australia	0.3225	464	82	6	38	174	133	22	59	3051	0	54	0	48.0	2.0	6.0	2.0	8.0	2.630000e+10	1.714000e+12	480	263000.0	1005000.0	1.821000e+09	10808002.0	23470145.0	1
10	Bangladesh	0.7066	177	44	0	11	57	67	0	276	1230	18	419	72	112.0	0.0	2.0	0.0	8.0	3.800000e+09	5.026000e+10	18	3666.0	112000.0	2.800000e+07	78913289.0	159453001.0	1
11	Belarus	0.8179	202	39	68	4	28	63	21	532	1560	482	228	238	0.0	0.0	0.0	0.0	0.0	6.237028e+08	3.992000e+10	65	32000.0	190000.0	1.980000e+08	4982905.0	9527543.0	1
17	Brazil	0.1988	715	43	78	126	198	242	12	437	1820	132	565	84	112.0	0.0	6.0	0.0	7.0	2.780000e+10	5.474000e+11	4093	2622000.0	3020000.0	1.300000e+10	107764996.0	208846892.0	1
18	Bulgaria	0.8916	63	13	4	5	20	25	2	710	2000	506	350	192	29.0	0.0	0.0	0.0	4.0	1.079000e+09	4.206000e+10	68	1000.0	115000.0	1.500000e+07	3278916.0	7057504.0	1
22	Canada	0.3712	385	62	0	38	135	145	0	80	1973	0	133	0	64.0	0.0	4.0	0.0	12.0	2.250000e+10	1.608000e+12	1467	3977000.0	2500000.0	1.697000e+11	16110865.0	35881659.0	1
25	Chile	0.7668	282	44	0	30	93	92	0	300	1430	48	158	10	69.0	0.0	4.0	0.0	10.0	4.250000e+09	1.834000e+11	481	3244.0	325000.0	1.500000e+08	8657902.0	17925262.0	1
27	Colombia	0.6045	469	21	38	76	80	224	0	0	595	6	110	0	453.0	0.0	11.0	0.0	4.0	1.060000e+10	1.246000e+11	836	853600.0	300000.0	2.002000e+09	24084498.0	48168996.0	1
32	Denmark	0.7878	113	33	0	4	38	35	0	57	972	22	0	0	74.0	0.0	0.0	0.0	9.0	4.760000e+09	4.848000e+11	80	137100.0	162000.0	4.906000e+08	2590457.0	5809502.0	1
35	Egypt	0.1872	1054	215	88	59	387	294	81	4295	11700	1139	2189	1084	316.0	2.0	8.0	0.0	7.0	1.120000e+10	7.747000e+10	83	589400.0	832000.0	4.400000e+09	42946553.0	99413317.0	1
39	Finland	0.8498	194	55	0	11	105	127	0	200	2050	100	627	75	246.0	0.0	0.0	0.0	0.0	3.570000e+09	1.506000e+11	148	0.0	205000.0	0.000000e+00	2307420.0	5537364.0	1
40	France	0.1702	1229	269	0	121	187	589	62	528	6028	109	12	13	180.0	4.0	9.0	11.0	11.0	4.150000e+10	5.360000e+12	464	15170.0	1600000.0	7.235000e+07	30111868.0	67364357.0	1
43	Germany	0.2186	712	128	74	71	26	386	56	245	4583	108	0	38	80.0	0.0	6.0	0.0	9.0	5.000000e+10	5.326000e+12	539	43410.0	2500000.0	1.454000e+08	37010559.0	80457737.0	1
45	Greece	0.5311	566	187	0	15	143	231	29	1355	3691	547	463	152	116.0	0.0	11.0	0.0	13.0	4.844000e+09	5.066000e+11	77	2753.0	350000.0	1.000000e+07	4993347.0	10761523.0	1
50	Indonesia	0.2544	462	41	39	54	109	177	16	313	1178	153	366	36	282.0	0.0	5.0	0.0	7.0	7.600000e+09	3.444000e+11	673	801700.0	1630000.0	3.230000e+09	130868127.0	262787403.0	1
51	Iran	0.2191	509	155	23	62	94	100	12	2056	4300	570	2088	1935	398.0	0.0	34.0	0.0	7.0	1.960000e+10	7.995000e+09	319	4469000.0	2000000.0	1.584000e+11	47324105.0	83024745.0	1
52	Iraq	0.7911	348	26	33	16	102	186	40	309	4739	44	120	30	60.0	0.0	0.0	0.0	0.0	1.730000e+09	7.302000e+10	102	4454000.0	825000.0	1.425000e+11	16399240.0	40194216.0	1
54	Israel	0.3111	589	259	18	15	154	146	48	2760	10275	650	300	100	65.0	0.0	5.0	0.0	0.0	2.000000e+10	8.866000e+10	47	390.0	250000.0	1.273000e+07	3647983.0	8424904.0	1
55	Italy	0.2111	860	99	110	38	185	439	59	200	6947	54	108	21	249.0	2.0	8.0	4.0	12.0	2.780000e+10	2.444000e+12	129	77530.0	1300000.0	5.567000e+08	28011003.0	62246674.0	1
57	Japan	0.1501	1561	279	12	60	427	637	119	1004	3130	238	500	99	155.0	4.0	20.0	40.0	0.0	4.900000e+10	3.240000e+12	175	3841.0	4000000.0	4.412000e+07	53873803.0	126168156.0	1
58	Jordan	1.1441	278	44	4	16	75	146	47	1313	2897	461	72	88	37.0	0.0	0.0	0.0	0.0	2.600000e+09	2.934000e+10	18	22.0	110000.0	1.000000e+06	3346692.0	10458413.0	1
61	Kuwait	1.5701	85	27	0	2	13	40	16	367	745	98	0	27	38.0	0.0	0.0	0.0	0.0	6.831000e+09	4.724000e+10	7	2753000.0	345000.0	1.015000e+11	1641971.0	2916467.0	1
70	Malaysia	0.6546	170	26	13	18	40	65	0	74	1387	0	211	54	61.0	0.0	2.0	0.0	6.0	4.000000e+09	2.172000e+11	114	647900.0	835000.0	3.600000e+09	15201837.0	31809660.0	1
73	Mexico	0.6065	484	3	33	43	206	202	0	0	2199	0	265	0	189.0	0.0	0.0	0.0	4.0	7.000000e+09	4.458000e+11	1714	1981000.0	2000000.0	7.640000e+09	60460418.0	125959205.0	1
77	Morocco	0.8408	214	46	0	31	67	64	0	1443	2901	505	200	144	121.0	0.0	0.0	0.0	3.0	1.000000e+10	5.148000e+10	55	160.0	206000.0	6.840000e+05	17157065.0	34314130.0	1
79	Myanmar	0.5691	276	59	21	27	82	86	9	434	1300	108	1612	84	187.0	0.0	1.0	0.0	5.0	2.650000e+09	6.594000e+09	64	12000.0	42600.0	1.390000e+08	30258643.0	55622506.0	1
82	Netherlands	0.5919	168	61	1	4	27	75	28	18	1546	57	160	0	63.0	0.0	4.0	0.0	6.0	1.241900e+10	4.063000e+12	29	18170.0	840000.0	1.132000e+08	7820960.0	17151228.0	1
87	North Korea	0.3718	949	458	114	4	169	204	20	6045	10000	800	1000	2110	984.0	0.0	83.0	0.0	11.0	1.600000e+09	5.000000e+09	82	0.0	15500.0	0.000000e+00	13045878.0	25381085.0	1
91	Pakistan	0.2364	1372	356	90	49	513	346	56	2200	7330	429	1226	100	100.0	0.0	8.0	0.0	9.0	1.140000e+10	8.219000e+10	151	89720.0	575000.0	3.506000e+08	96344277.0	207862518.0	1
94	Peru	0.6219	273	17	38	40	77	99	16	146	552	24	231	70	60.0	0.0	5.0	0.0	7.0	2.560000e+09	6.625000e+10	191	43520.0	210000.0	4.730000e+08	15352302.0	31331228.0	1
96	Poland	0.3397	457	91	0	49	104	176	30	1069	2547	547	0	180	87.0	0.0	3.0	0.0	2.0	1.200000e+10	2.410000e+11	126	20070.0	585000.0	1.378000e+08	18826137.0	38420687.0	1
97	Portugal	0.8612	90	24	0	11	18	23	0	133	756	18	61	0	41.0	0.0	2.0	0.0	5.0	3.358000e+09	4.490000e+11	64	0.0	265000.0	0.000000e+00	4804949.0	10355493.0	1
100	Romania	0.6177	143	26	0	12	41	62	0	943	1369	0	808	243	48.0	0.0	0.0	0.0	3.0	5.050000e+09	9.597000e+10	45	73740.0	240000.0	6.000000e+08	11007501.0	21457116.0	1
102	Saudi Arabia	0.3034	879	270	81	49	208	283	34	1062	12825	705	1818	122	55.0	0.0	0.0	0.0	3.0	6.760000e+10	2.051000e+11	214	10130000.0	4000000.0	2.665000e+11	17703745.0	33091113.0	1
105	Singapore	0.7966	235	100	0	9	36	77	17	180	3000	48	77	24	40.0	0.0	0.0	0.0	6.0	1.120000e+10	5.661000e+11	9	0.0	1340000.0	0.000000e+00	4017314.0	5995991.0	1
109	South Africa	0.4985	226	17	0	23	88	91	12	195	2000	43	72	50	30.0	0.0	3.0	0.0	4.0	4.278000e+09	1.563000e+11	566	2000.0	605000.0	1.500000e+07	26250220.0	55380210.0	1
110	South Korea	0.1509	1649	414	71	41	298	803	112	2614	14000	3040	3854	575	234.0	0.0	19.0	12.0	12.0	4.400000e+10	3.846000e+11	111	0.0	2800000.0	0.000000e+00	25709049.0	51418097.0	1
112	Spain	0.3388	512	137	12	46	162	162	19	327	2023	95	140	0	77.0	1.0	3.0	0.0	11.0	1.510000e+10	2.094000e+12	150	2252.0	1226000.0	1.500000e+08	23136275.0	49331076.0	1
118	Syria	0.8241	456	199	20	5	67	165	27	4135	5370	575	2550	750	56.0	0.0	0.0	0.0	2.0	1.800000e+09	4.989000e+09	90	14000.0	260000.0	2.500000e+09	12450728.0	19454263.0	1

Military Power Clustering Project¶

PREFACE¶

Roadmap For The Data¶

TASKS¶

DATA SET¶

Import Libraries¶

Ingest Data¶

EDA¶

Implement basic steps to see how is your data looks like¶

Convert multi index columns to one level¶

Selecting Value-Related Columns¶

Check for missing values¶

Deal with missing values¶

Fix the dtypes¶

Drop the features that not affect military power¶

Visualy inspect the some features¶

Decriptive statistics¶

Data Preprocessing¶

Modelling¶

Hopkins Test¶

Clustering with K-means¶

Elbow Method¶

We use this method to find optimal k value by looking up to break point of graph.¶

Silhouette analysis¶

Model Building and label visualisation¶

Evaluate¶

Results¶

Hierarchical Clustering¶

Dendogram¶

Model Building and label visualisation¶

Results¶

REFERENCES¶